{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i>Copyright (c) Microsoft Corporation.</i>\n",
    "\n",
    "<i>Licensed under the MIT License.</i>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LightGBM: A Highly Efficient Gradient Boosting Decision Tree\n",
    "This notebook gives an example of how to train a LightGBM model to generate point forecasts for product sales in retail. We will train a LightGBM model based on the Orange Juice dataset.\n",
    "\n",
    "[LightGBM](https://github.com/Microsoft/LightGBM) is a gradient boosting framework that uses tree-based learning algorithms. [Gradient boosting](https://en.wikipedia.org/wiki/Gradient_boosting) is an ensemble technique in which models are added to the ensemble sequentially and at each iteration a new model is trained with respect to the error of the whole ensemble learned so far. More detailed information about gradient boosting can be found in this [tutorial paper](https://www.frontiersin.org/articles/10.3389/fnbot.2013.00021/full). Using this technique, LightGBM achieves great accuracy in many applications. Apart from this, it is designed to be distributed and efficient with the following advantages:\n",
    "* Fast training speed and high efficiency.\n",
    "* Low memory usage.\n",
    "* Support of parallel and GPU learning.\n",
    "* Capable of handling large-scale data.\n",
    "\n",
    "Due to these advantages, LightGBM has been widely used in a lot of [winning solutions](https://github.com/microsoft/LightGBM/blob/master/examples/README.md#machine-learning-challenge-winning-solutions) of machine learning competitions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Global Settings and Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "System version: 3.6.10 |Anaconda, Inc.| (default, Jan  7 2020, 21:14:29) \n",
      "[GCC 7.3.0]\n",
      "LightGBM version: 2.3.0\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import math\n",
    "import datetime\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import lightgbm as lgb\n",
    "import scrapbook as sb\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from fclib.common.utils import git_repo_path\n",
    "from fclib.models.lightgbm import predict\n",
    "from fclib.evaluation.evaluation_utils import MAPE\n",
    "from fclib.common.plot import plot_predictions_with_history\n",
    "from fclib.dataset.ojdata import download_ojdata, split_train_test\n",
    "from fclib.dataset.ojdata import FIRST_WEEK_START\n",
    "from fclib.feature_engineering.feature_utils import (\n",
    "    week_of_month,\n",
    "    df_from_cartesian_product,\n",
    "    combine_features,\n",
    ")\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "print(\"System version: {}\".format(sys.version))\n",
    "print(\"LightGBM version: {}\".format(lgb.__version__))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameter Settings\n",
    "\n",
    "In what follows, we define parameters related to the model and feature engineering. LightGBM supports both classification models and regression models. In our case, we set the objective function to `mape` which stands for mean absolute percentage error (MAPE) since we will build a regression model to predict product sales and evaluate the accuracy of the model using MAPE.\n",
    "\n",
    "Generally, we can adjust the number of leaves (`num_leaves`), the minimum number of data in each leaf (`min_data_in_leaf`), the maximum number of boosting rounds (`num_rounds`), the learning rate of trees (`learning_rate`) and `early_stopping_rounds` (to avoid overfitting) in the model to get better performance. Besides, we can also adjust other supported parameters to optimize the results. [In this link](https://github.com/Microsoft/LightGBM/blob/master/docs/Parameters.rst), a list of all the parameters is given. In addition, advice on how to tune these parameters can be found [in this URL](https://github.com/Microsoft/LightGBM/blob/master/docs/Parameters-Tuning.rst).\n",
    "\n",
    "We will use historical weekly sales, date-time information, and product information as input features to train the model. Prior sales are used as lag features and `lags` contains the lags where each number indicates the number of time steps (i.e., weeks) that we shift the data backwards to get the historical sales. We also use the average sales within a certain time window in the past as a moving average feature. `window_size` controls the size of the moving window. Apart from these parameters, we use `use_columns` and `categ_fea` to denote all other features that we leverage in the model and the categorical features, respectively.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Use False if you've already downloaded and split the data\n",
    "DOWNLOAD_SPLIT_DATA = True\n",
    "\n",
    "# Data directory\n",
    "DATA_DIR = os.path.join(git_repo_path(), \"ojdata\")\n",
    "\n",
    "# Forecasting settings\n",
    "N_SPLITS = 1\n",
    "HORIZON = 2\n",
    "GAP = 2\n",
    "FIRST_WEEK = 40\n",
    "LAST_WEEK = 138\n",
    "\n",
    "# Parameters of LightGBM model\n",
    "params = {\n",
    "    \"objective\": \"mape\",\n",
    "    \"num_leaves\": 124,\n",
    "    \"min_data_in_leaf\": 340,\n",
    "    \"learning_rate\": 0.1,\n",
    "    \"feature_fraction\": 0.65,\n",
    "    \"bagging_fraction\": 0.87,\n",
    "    \"bagging_freq\": 19,\n",
    "    \"num_rounds\": 940,\n",
    "    \"early_stopping_rounds\": 125,\n",
    "    \"num_threads\": 16,\n",
    "    \"seed\": 1,\n",
    "}\n",
    "\n",
    "# Lags, window size, and feature column names\n",
    "lags = np.arange(2, 20)\n",
    "window_size = 40\n",
    "used_columns = [\"store\", \"brand\", \"week\", \"week_of_month\", \"month\", \"deal\", \"feat\", \"move\", \"price\", \"price_ratio\"]\n",
    "categ_fea = [\"store\", \"brand\", \"deal\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation\n",
    "\n",
    "We need to download the Orange Juice data and split it into training and test sets. By default, the following cell will download and spit the data. If you've already done so, you may skip this part by switching `DOWNLOAD_SPLIT_DATA` to `False`.\n",
    "\n",
    "We store the training data and test data using dataframes. The training data includes `train_df` and `aux_df` with `train_df` containing the historical sales up to week 135 (the time we make forecasts) and `aux_df` containing price/promotion information up until week 138. Here we assume that future price and promotion information up to a certain number of weeks ahead is predetermined and known. The test data is stored in `test_df` which contains the sales of each product in week 137 and 138. Assuming the current week is week 135, our goal is to forecast the sales in week 137 and 138 using the training data. There is a one-week gap between the current week and the first target week of forecasting as we want to leave time for planning inventory in practice.\n",
    "\n",
    "The setting of the forecast problem are defined in `fclib.dataset.ojdata.split_train_test` function. We can change this setting (e.g., modify the horizon of the forecast or the range of the historical data) by passing different parameters to this functions. Below, we split the data into `n_splits=1` splits, using the forecasting settings listed above in the **Parameter Settings** section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data already exists at the specified location.\n"
     ]
    }
   ],
   "source": [
    "if DOWNLOAD_SPLIT_DATA:\n",
    "    download_ojdata(DATA_DIR)\n",
    "    train_df_list, test_df_list, aux_df_list = split_train_test(\n",
    "        DATA_DIR,\n",
    "        n_splits=N_SPLITS,\n",
    "        horizon=HORIZON,\n",
    "        gap=GAP,\n",
    "        first_week=FIRST_WEEK,\n",
    "        last_week=LAST_WEEK,\n",
    "        write_csv=True,\n",
    "    )\n",
    "\n",
    "    # Split returns a list, extract the dataframes from the list\n",
    "    train_df = train_df_list[0].reset_index()\n",
    "    test_df = test_df_list[0].reset_index()\n",
    "    aux_df = aux_df_list[0].reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering\n",
    "\n",
    "Next we extract a number of features from the data for training the forecasting model including\n",
    "* datetime features including week, week of the month, and month\n",
    "* historical weekly sales of each orange juice in recent weeks\n",
    "* average sales of each orange juice during recent weeks\n",
    "* other features including `store`, `brand`, `deal`, `feat` columns and price features\n",
    "\n",
    "Note that the logarithm of the unit sales is stored in a column named `logmove` both for `train_df` and `test_df`. We compute the unit sales `move` based on this quantity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   store  brand  week   logmove  constant    price1    price2    price3  \\\n",
      "0      2      1    40  9.018695         1  0.060469  0.060497  0.042031   \n",
      "1      2      1    46  8.723231         1  0.060469  0.060312  0.045156   \n",
      "2      2      1    47  8.253228         1  0.060469  0.060312  0.045156   \n",
      "\n",
      "     price4    price5    price6    price7    price8    price9   price10  \\\n",
      "0  0.029531  0.049531  0.053021  0.038906  0.041406  0.028906  0.024844   \n",
      "1  0.046719  0.049531  0.047813  0.045781  0.027969  0.042969  0.042031   \n",
      "2  0.046719  0.037344  0.053021  0.045781  0.041406  0.048125  0.032656   \n",
      "\n",
      "    price11  deal  feat     profit  move  \n",
      "0  0.038984     1   0.0  37.992326  8256  \n",
      "1  0.038984     0   0.0  30.126667  6144  \n",
      "2  0.038984     0   0.0  30.000000  3840  \n",
      "\n",
      "Number of missing rows is 6204\n",
      "\n",
      "Maximum training week number is 135\n",
      "\n",
      "    store  brand  week  week_of_month  month  deal  feat     move     price  \\\n",
      "19      2      1    59              4     10   1.0   0.0   8384.0  0.055625   \n",
      "20      2      1    60              1     11   0.0   0.0   5952.0  0.055625   \n",
      "21      2      1    61              2     11   1.0   0.0   6848.0  0.055625   \n",
      "22      2      1    62              3     11   0.0   0.0   9216.0  0.060469   \n",
      "23      2      1    63              4     11   1.0   0.0  12160.0  0.046719   \n",
      "\n",
      "    price_ratio     ...       move_lag11  move_lag12  move_lag13  move_lag14  \\\n",
      "19     1.279808     ...           8000.0      3840.0      6144.0      8256.0   \n",
      "20     1.304536     ...           8000.0      8000.0      3840.0      6144.0   \n",
      "21     1.257614     ...           8896.0      8000.0      8000.0      3840.0   \n",
      "22     1.369765     ...           7168.0      8896.0      8000.0      8000.0   \n",
      "23     1.130823     ...          10880.0      7168.0      8896.0      8000.0   \n",
      "\n",
      "    move_lag15  move_lag16  move_lag17  move_lag18  move_lag19    move_mean  \n",
      "19      8256.0      8256.0      8256.0      8256.0      8256.0  7847.111111  \n",
      "20      8256.0      8256.0      8256.0      8256.0      8256.0  7744.000000  \n",
      "21      6144.0      8256.0      8256.0      8256.0      8256.0  7776.000000  \n",
      "22      3840.0      6144.0      8256.0      8256.0      8256.0  7689.142857  \n",
      "23      8000.0      3840.0      6144.0      8256.0      8256.0  7650.909091  \n",
      "\n",
      "[5 rows x 29 columns]\n"
     ]
    }
   ],
   "source": [
    "# Load training data\n",
    "train_df[\"move\"] = train_df[\"logmove\"].apply(lambda x: round(math.exp(x)))\n",
    "print(train_df.head(3))\n",
    "print(\"\")\n",
    "train_df = train_df[[\"store\", \"brand\", \"week\", \"move\"]]\n",
    "\n",
    "# Create a dataframe to hold all necessary data\n",
    "store_list = train_df[\"store\"].unique()\n",
    "brand_list = train_df[\"brand\"].unique()\n",
    "week_list = range(FIRST_WEEK, LAST_WEEK + 1)\n",
    "d = {\"store\": store_list, \"brand\": brand_list, \"week\": week_list}\n",
    "data_grid = df_from_cartesian_product(d)\n",
    "data_filled = pd.merge(data_grid, train_df, how=\"left\", on=[\"store\", \"brand\", \"week\"])\n",
    "\n",
    "# Get future price, deal, and advertisement info\n",
    "data_filled = pd.merge(data_filled, aux_df, how=\"left\", on=[\"store\", \"brand\", \"week\"])\n",
    "\n",
    "# Create relative price feature\n",
    "price_cols = [\n",
    "    \"price1\",\n",
    "    \"price2\",\n",
    "    \"price3\",\n",
    "    \"price4\",\n",
    "    \"price5\",\n",
    "    \"price6\",\n",
    "    \"price7\",\n",
    "    \"price8\",\n",
    "    \"price9\",\n",
    "    \"price10\",\n",
    "    \"price11\",\n",
    "]\n",
    "data_filled[\"price\"] = data_filled.apply(lambda x: x.loc[\"price\" + str(int(x.loc[\"brand\"]))], axis=1)\n",
    "data_filled[\"avg_price\"] = data_filled[price_cols].sum(axis=1).apply(lambda x: x / len(price_cols))\n",
    "data_filled[\"price_ratio\"] = data_filled[\"price\"] / data_filled[\"avg_price\"]\n",
    "data_filled.drop(price_cols, axis=1, inplace=True)\n",
    "\n",
    "# Fill missing values\n",
    "print(\"Number of missing rows is {}\".format(data_filled[data_filled.isnull().any(axis=1)].shape[0]))\n",
    "print(\"\")\n",
    "data_filled = data_filled.groupby([\"store\", \"brand\"]).apply(lambda x: x.fillna(method=\"ffill\").fillna(method=\"bfill\"))\n",
    "\n",
    "# Create datetime features\n",
    "data_filled[\"week_start\"] = data_filled[\"week\"].apply(lambda x: FIRST_WEEK_START + datetime.timedelta(days=(x - 1) * 7))\n",
    "data_filled[\"year\"] = data_filled[\"week_start\"].apply(lambda x: x.year)\n",
    "data_filled[\"month\"] = data_filled[\"week_start\"].apply(lambda x: x.month)\n",
    "data_filled[\"week_of_month\"] = data_filled[\"week_start\"].apply(lambda x: week_of_month(x))\n",
    "data_filled[\"day\"] = data_filled[\"week_start\"].apply(lambda x: x.day)\n",
    "data_filled.drop(\"week_start\", axis=1, inplace=True)\n",
    "\n",
    "# Create other features (lagged features, moving averages, etc.)\n",
    "features = data_filled.groupby([\"store\", \"brand\"]).apply(\n",
    "    lambda x: combine_features(x, [\"move\"], lags, window_size, used_columns)\n",
    ")\n",
    "train_fea = features[features.week <= max(train_df.week)].reset_index(drop=True)\n",
    "print(\"Maximum training week number is {}\".format(max(train_fea[\"week\"])))\n",
    "print(\"\")\n",
    "\n",
    "# Drop rows with NaN values\n",
    "train_fea.dropna(inplace=True)\n",
    "print(train_fea.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training\n",
    "\n",
    "We then train a LightGBM model to predict the sales. After the model is trained, we apply it to generate forecasts for the target weeks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training LightGBM model...\n",
      "Training until validation scores don't improve for 125 rounds\n",
      "[50]\ttraining's mape: 0.311722\n",
      "[100]\ttraining's mape: 0.285767\n",
      "[150]\ttraining's mape: 0.274155\n",
      "[200]\ttraining's mape: 0.265833\n",
      "[250]\ttraining's mape: 0.260146\n",
      "[300]\ttraining's mape: 0.255795\n",
      "[350]\ttraining's mape: 0.252398\n",
      "[400]\ttraining's mape: 0.24973\n",
      "[450]\ttraining's mape: 0.247442\n",
      "[500]\ttraining's mape: 0.245553\n",
      "[550]\ttraining's mape: 0.243832\n",
      "[600]\ttraining's mape: 0.242123\n",
      "[650]\ttraining's mape: 0.240704\n",
      "[700]\ttraining's mape: 0.239503\n",
      "[750]\ttraining's mape: 0.238308\n",
      "[800]\ttraining's mape: 0.237258\n",
      "[850]\ttraining's mape: 0.236335\n",
      "[900]\ttraining's mape: 0.235512\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[940]\ttraining's mape: 0.234885\n",
      "\n",
      "Prediction results:\n",
      "      store  brand  week   move\n",
      "0         2      1   137   9151\n",
      "1         2      1   138  24055\n",
      "2         2      2   137   4879\n",
      "3         2      2   138  12035\n",
      "4         2      3   137   2156\n",
      "5         2      3   138   2414\n",
      "6         2      4   137   3531\n",
      "7         2      4   138   9902\n",
      "8         2      5   137  16393\n",
      "9         2      5   138   6252\n",
      "10        2      6   137   2911\n",
      "11        2      6   138   1499\n",
      "12        2      7   137   2902\n",
      "13        2      7   138   2266\n",
      "14        2      8   137   1217\n",
      "15        2      8   138   1317\n",
      "16        2      9   137    684\n",
      "17        2      9   138    846\n",
      "18        2     10   137  11737\n",
      "19        2     10   138   4769\n",
      "20        2     11   137   4041\n",
      "21        2     11   138   4094\n",
      "22        5      1   137   8838\n",
      "23        5      1   138  24861\n",
      "24        5      2   137   6825\n",
      "25        5      2   138  12805\n",
      "26        5      3   137   2810\n",
      "27        5      3   138   2870\n",
      "28        5      4   137   3099\n",
      "29        5      4   138   6092\n",
      "...     ...    ...   ...    ...\n",
      "1796    134      8   137    614\n",
      "1797    134      8   138    531\n",
      "1798    134      9   137   1023\n",
      "1799    134      9   138   1031\n",
      "1800    134     10   137   7678\n",
      "1801    134     10   138   3857\n",
      "1802    134     11   137   9551\n",
      "1803    134     11   138   9781\n",
      "1804    137      1   137  15409\n",
      "1805    137      1   138  27916\n",
      "1806    137      2   137  13290\n",
      "1807    137      2   138  19984\n",
      "1808    137      3   137   2825\n",
      "1809    137      3   138   2975\n",
      "1810    137      4   137   3550\n",
      "1811    137      4   138   6959\n",
      "1812    137      5   137  24446\n",
      "1813    137      5   138  11950\n",
      "1814    137      6   137   6522\n",
      "1815    137      6   138   3812\n",
      "1816    137      7   137   4839\n",
      "1817    137      7   138   3211\n",
      "1818    137      8   137   1363\n",
      "1819    137      8   138   1540\n",
      "1820    137      9   137    443\n",
      "1821    137      9   138    459\n",
      "1822    137     10   137  14673\n",
      "1823    137     10   138   7523\n",
      "1824    137     11   137   6825\n",
      "1825    137     11   138   6676\n",
      "\n",
      "[1826 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "# Create training set\n",
    "dtrain = lgb.Dataset(train_fea.drop(\"move\", axis=1, inplace=False), label=train_fea[\"move\"])\n",
    "\n",
    "# Train GBM model\n",
    "print(\"Training LightGBM model...\")\n",
    "bst = lgb.train(params, dtrain, valid_sets=[dtrain], categorical_feature=categ_fea, verbose_eval=50)\n",
    "print(\"\")\n",
    "\n",
    "# Generate forecasts\n",
    "test_fea = features[(features.week >= min(test_df.week)) & (features.week <= max(test_df.week))].reset_index(drop=True)\n",
    "idx_cols = [\"store\", \"brand\", \"week\"]\n",
    "pred = predict(test_fea, bst, target_col=\"move\", idx_cols=idx_cols).sort_values(by=idx_cols).reset_index(drop=True)\n",
    "print(\"Prediction results:\")\n",
    "print(pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation\n",
    "\n",
    "To evaluate the model performance, we compute MAPE of the forecasts below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/scrapbook.scrap.json+json": {
       "data": 35.58242942761259,
       "encoder": "json",
       "name": "MAPE",
       "version": 1
      }
     },
     "metadata": {
      "scrapbook": {
       "data": true,
       "display": false,
       "name": "MAPE"
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE of the forecasts is 35.58242942761259\n"
     ]
    }
   ],
   "source": [
    "# Evaluate prediction accuracy\n",
    "test_df[\"actual\"] = test_df[\"logmove\"].apply(lambda x: round(math.exp(x)))\n",
    "test_df.drop(\"logmove\", axis=1, inplace=True)\n",
    "combined = pd.merge(pred, test_df, on=[\"store\", \"brand\", \"week\"], how=\"left\")\n",
    "metric_value = MAPE(combined[\"move\"], combined[\"actual\"]) * 100\n",
    "sb.glue(\"MAPE\", metric_value)\n",
    "print(\"MAPE of the forecasts is {}\".format(metric_value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Result Visualization\n",
    "\n",
    "In the next, we plot out the feature importance learned by the model and the forecast results of a few sample store-brand combinations. Note that there could be gaps in the curve of actual sales due to missing sales data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot feature importances\n",
    "ax = lgb.plot_importance(bst, max_num_features=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HbzfHZmH2evPdD4T8Qjuwd8r8p5P17GD7fbuc9AKJl4CGjO/j4vwPkVLA2hd/hIKnF3IczyV57oO+uDeTY/Ji9jEh1A7weN2PzZo2PF5X9xWxr7cSCl1GZoT9NG7j31PmT45D9nkv6prup3NzQZHr6lVaSw/SF+DmGNblJV7GcXwtK2x5/KshFBA5IT+d9x7Os/9O4QUYq+hcGPFHMgqtM+abAnwNOIjwzJ5AqAF0b1zuOWBMjm2cHef5eG+vE/2V95+akAw958dPJ2Sa/kTIVP0yZd4nPL3Zw9z4Od7Sh5yaHD/3gdD3BWFouZc9vcOuhRnxymdO/PxjgfMX66D4eW+O6fcSqpi+ia4dSKY1K0l6Vp5YZDwezxGeHPtZOY79G+PnPoSmL/cT3tp8OVZNvY2QaVvq6VX3B8K1wP8CT5rZDTGOD3l69emeOIiQSViYMu1+Qkb9TSnTlrv7aynh3TKz/YAvEhLYHQgZhEwF94GSY/2jCZmYBkJnu2mzNRPvNwB3f8nMXiCMMDSMWMhEqBZ9PyHDNJ/whugIQs2jXNd8tmsJTRmeNLPfxPU94inVNS2MzPAZQpXlNxKaZWXuQK5jk3YvJZ2bpo3wkFQF7tLunLCvD6eEL4yfaddCpkMImedcQ+yNjJ+p7cD7WF8cl+QZsluO/UnaI+9DeF50p9B7uag0I0uuZ2GaNxJ+EDwH/HeOe2VL1nb64nmU9DOyLs9892cHuPt6M1tKaC6xD12bE3XZ/548E9ietqXF4QUze5nwYyyfJA2+w93bC5i/KBn5hVc8vZPW5DmVdt/mStdeZvs1iLtvMLPfEwr4l5rZbwl5oce8+yZA2XE1QnPE0wjnYyLhWZHI1U9AvvS9L+7NRNoxSZ4Z/3D3jZkT3L3NzFaT8iw1s/cSmmXMJtS0yf4NUc/2jtdzXm+EY92pGUgPr+mcenFuitIHaW1P0pe5hIKeE8zshJRlqoDJZlbn7o0Z4aMITVfmAme7+3eL2NVec/dpEPplI9TQ+TawxMyO8djsOc73Gl2Hon7AQl9JDxKaIP0HoRlotqQZTH0fR1/KjAowhhh3L7jTOkJpaZoks/au+JdLbfxMOmzK1Z9Aru2kSTon6q+hVZO45hr9JAlP6yTp9ZSwJJEenjKtO/mO/Rl5lq+FjozaHEJbyfcTqtMCNJjZjwi1RrYVGbdecfeLzayB0GfIpwl9AriZ3Q980dP7FynGeGCtp3Qy5e6tcdtTUpYr5joEIB7bewnP0nsIb6I2EKt8Et6oFdzxWA4TCZmeyRRe0EeMzxmEjOS2uPw97r7RzP7M9uEL52fMX4jPEZpffJTQJvrLQKuZ3Qb8l7svg46+CO4FDiU0p7mB0EdIcr2dT+5jk9Z2tbWAaSNTpjXk+FGTnO9uO5Rj+z13SPzLpbabaX2lL45Lsj9pGd9MefeniHu52DQjUzH3ZbKdPen+XunYTh89j7bEzy4d02bJlwamXYtp+9+TZ0Ih6XAhBRiVlgZDuA+yO289kfC29oNs759jq5ndCHzB3fP1fwShP5HPxjjdQTgmybVwGrmPZ770vdf3ZoYuz4WYDqZOi1rJepaa2acJPxjXEWo5/pPQFMIJ/XPMovPzPOf1FgtJGrOCe5rO5dLTc9MTvUlre5K+1BHyHPmOUy2hxmTm94MIeZQ78izbb+K9dbOZ/YXQx94vgP0LWK7VzH5GKMB4G+kFGKPi55aUaTKIqABDuuM5wpNE7zPufmkB60nmzzVedJce2LuRZFB2on+Gd0vimitOO2TN11/yHftZ3k1nRp1W5L4COD2+kdiX0InUJwkl3MMI7WEh/OiGlOeCFdGrdYFx+gXwi7jetxDe0H8UuMPM9ulJTYgM6wmdGo7MLpwxsxGEkvm0zq1yHfPu/DchwTzC3RdmbescQgFGbyXnfIm7H9TtnJ3dS8hUvZPtb5vuzfg8x8wmETJV6wl9huQVCwN+APzAzKYQaiSdRMh07xc7HGsm7PuhwNXuflrmOsxsB/omk1qIejMbnlKIkdzj+e7lZPol7v75vo1aSST7c6y739rblRV4LxebZnTaRBHzJtu52d0/UPAGev88SqanjviSIV8amPZjM23/e/JMyEyHn+wmDvlkpsH9YUDSYHffQmxSa2a7EH4QnUbo+HY68Nbulo/Pvk8TCmffkl2TwbofnShf+t4n92ZfienmhYSCl4M8DHGfOX1uymKZ11unjsLNbDjhXnklZf5i07m0+Pbm3PREb9LanqQv6wlNPyYVGc81hCZYtwL3mdmRffDCqMdi7ZWngAPNrN7dGwpYLKkZNybH9OQZ3Js8pFQADaMqPfFo/Ow2gU/ExGMZsJOZ7Z4yy+E92HYhQ1QmP1iKqf2wJH4enmN6El7Qj71+UNSxz+TBk+7+Q7a/BT0uY5ak+vMuKYvPLnJz7RRw3N39dXe/zd3PIPR5Moke7FuWJYRn29tSpr0txquvzt8ehNoeC1Omvb0vNuDuTYQfHPvFTFChkvai8wmFVi+4+4tx2j2EY3QK4W31wp40KXL319z9Jnf/t7i93dn+JmWP+PnblEX75NgUaAThR2m2w+PnkpRpmR4nXM+9vS4TbRRfI6sv9fgZ0p0893K/bDPFM8RRC2INoKL04nmUFCbvnWe+Ltd9HMHkQEIfAk8XGM+ePBOSZ15aHN5A+nM/TXIuj7KUIWlTtMVtFHTNx/zC84T8QtrwiskQn32WBrv7y+5+LaGG4nPAPMsx/HCGNxCeoXem/EDeOU4v1kDdJ8WqJ9R4eTil8CJ5q58t5/VG2L9OL0p6kc6l6cm56Ul+MdGbtLYn6cujwMTYfLUo7n4PodPhEcDdOQqfBlIyhHOh+Y+kCVuu0fOSZ3ChI3tJhVIBhhQtltj+CfiAmX00bR4zOyCWgid+TrjevpOZ6bEwHNuni9j8jwnVG88zs31TtpvZbnMdIVHZNXu+bjxEGHpvnpkdn7Xu4wk/gP9BaIdXCj8nZNDPN7NDsyea2TDLGLfczPa3jPHlMyRvAjPb+y4iJKQfjO1Rk3VMAoptK9lIjgyxmR0d3+hkS66Xgtsg53Bl/PxW1n6MJrS5hDAaQV9YTqjtMTMz0MxOZ3tznb5wMaFd65VptWHMbKKFPk46xLfGTxI6pn0bnautPkz4wZQMRVhQ/xdmVm1m8y2rgXL8sZhkOpPztzx+Hp417xuA7xSyvT70LQtDOSdxmESoPQPhnsopHsdrgdlmdl7atWtmu8dnWSEaCe2TR+Wds3/cQviB+Ekze0/aDGY2N/PeyaXQe7mHaUbRPAyv+EPCW/pL046xme2QmXb00fPoScKbwTl55jvFzLL7briAUN3+Vzn6nMql2GfCtYTq7f+ZmSbE9Ph7FJgfdPfFhOfHgYQmGNnbrTOzzKY0SRX2YtLhKwnNCb6XWfBhZvVsrzF4ZdqChTCzyWb25pRJYwj99LSSv4+E5fFzXlYcawkdWPakhnOf3Zt97DXCfXBw3L8kLiMJtfHS+hu4Kn5+JbNAIl4b38qxnaLTuRyWx89izk1PrlOgd2ltD9OXS+LnT81sx5T5x1ho3porvn8ivMRy4E4z67cXCma2t5l1qU0V86oXEZ6zD7v7uoxpb7YwlHL2Mu8gNGEFSOu3D8IzuIFQ+0YGMTUhkZ76IOFBfIWFtpGPEX5Y70wYi3p/QkdBSTWu/yW87f9X4C9mdgch03YioTPM9xeyUXd/yszOIgz9tcTMbiG8Makj1BLYSHxD4+5NZvYY8FYzu5ZQ8NAG3Jqr+YW7u5mdSmjjeUNc/zPAXjH+G4GP9EfnZYVw98ZYkHIz8KiZ3UNIONsJCe9cwrFIMpDvBC42s4cJ+/Ea4RwdG5f5Xsa6X43H6RRCx2Z/IPTW/h7COcrX2WGme4CTLHSUtpiQIXzA3R8Arie0NX6QkNEwwtuHQ+K8dxdzTLK5+3Vmdizwb4SO+X7H9na6MwgjEFzbm21k+D6hoOJBM/s1oWrnbEKzihuB47tZtmDufqWZHUxop/98vH/+SSg0mEHINP2c0MFapnvYXiOiI1Pl7s1m9hDF938xinB+lsd76yXCtfYuQidjt7p78hb594SaV583swMINR12JfTM/gd6kFHsoVcJbbP/bma3Etp2H0/4kfujeE3m8ynC27OvEX6EPkho270jYb8PIQzf+2LONWx3T5z/djN7gNAx3RPu/vui9qqH3H2bmX2A0Ab6D/HZsJTwA2WXGLc3EI5Pvh/vxdzLxaYZPfV1Qnv8jwPvM7N7CVXVpxDO4WGEETme6sE+pIrpxs3AmRaaUKU10YDQ+fRD8VnxKuE5MS9u98vF7GSxzwR3X25mXyakxUssdFi6nvD8mkCoRTKzy4bSfZjQCe43zexf4/9GOL5HEt6CLo/z3kNoXnaThX5ytgAvufs13az/fwi1LI8FnojLjY7rmUIYkaE3LxF2IqSfTxNqCrxMSOuOITRduTT7zX02d19lZtcTms8tNbM7CXmadxF+sC4lFPIUrI/vzT7j7u1mdinhGv1bzBdVEfJakwhDWx6RtcxDZvZDQofPf7fQt8g2wjldR0ofJ71I57LX05Nz8yzhOXGSmbXE7Tpwjbu/lP8o9SqtLSp9cfd74r38LeC5eH+8SOjjYjdCrZcHCTUtUrn7Y7FA4C7gNjM7zt3vyreTZrY3XZ9VE83sqozvX8hoDnI0oSDyAULhXCPhBdrbCdfyKrr26fYdQk2chYQh2yE8m94R/z/P3bt0zG1mexHyFQtyNL2TwcTLYCgU/fX/H3Qd472beafH+a/KM99YQqnyYsI44FsID9E/EIaVG5M1/zhCCfsrhETkGeC/CA+xLtsjZRjVjGlzCVXTXyO8KVlJGC/7+Kz59iD8kGok/GB34jChdDNsFqHA4hpCIrstfv4S2Ctl3gvieg7v6bHMmP+0zDjmOUf/Ryi82Uro0+GZGOfjMubbJx7zRYQ3hM2EjOWNhLah2eutJhRqrIjHdRlwDqGw0yl8GNUpwHWERLgt8zgTMh83E6oAbib0Gr0E+BIp48znuaYX5pg2jJAJWhS3sTlep58kZdiw7tZVQDyOIVTp3Ej4QXYn29tT5z2XxVwrcVv/L+O6X0WogvoN0occTIbRbSdriNh4Xh1YVcS+jozn6Y+EDN7WeF09Gs9rVdb8uxDeLiUdqD0Zly/qesp3b5DjXmb70HHjgctiPJoJVfU/TdYQit2dA0KG/VOEN2rr43r+SciQfhaoK/AYjiHUJFvB9mGsr8qY3u/HJeMe/TbhTdVmwjP8OcKz4cOkDEOaso6i7mWKSDO626989y3hx/Qp8dysJdwrrxAy9ecCu/R0H7qJSzLc4XdSpnWcv7hfS+O+ryH8INshZZmFFJBmU/wz4WTCj/bk3v0l4YdSl+3luX7qCD80no3rej3u10XA6Iz5hgPfjMd3W/Y5I8ewwoSC0XPj9bmF8Hx9EDg5Zd7pdPPszN43QoHNVwkFaskz4dU438kUOLQqoVDlIkI6uZVQEHJZPDZFHc9+uDfzHZPu0s8u54TwzP48oeBvS7zOriH8YL6KlLwa4T78FOF520zIp11GeB6nnveeXNN9cW7iMocQnhnr2Z5fPLzA7fUqraUH6Quh8PPX8bi2EO7npYQ83+x85zSG7x+P71bgvQXsZ3INd/c3PWv9l8V4NRDSvPXAnwnPxUkp2zg9nv/lhGs/ORY3kDIkesZy34zbP7CQc6a/yv6zeNJFREQGFTNbDuDu00sbExkK4hvjWcAMD51EJuEXEDqt7dLZr4iI9I6FJqIvAE+7+ztLHR/pf+oDQ0RERKT3vkDoD+CsUkdERGQI+QSh+dd/lToiMjBUgCEiIiLSS+7+N8Lwq1tLHRcRkSGkGTjd3Z8odURkYKgTTxEREZE+4O6/KHUcRESGEnf/canjIANLfWCIiIiIiIiISNlTExIRERERERERKXsqwBARERERERGRsqcCDBEREREREREpeyrAEBEREREREZGypwIMERERERERESl7KsAQERERERERkbKnAgwRERERERERKXsqwBARERERERGRsqcCDBEREREREREpeyrAEBEREREREZGypwIMERERERERESl7KsAQERERERERkbKnAgwREREREZCwTjEAACAASURBVBERKXsqwBCRQcPMLjCzXw7V7YuIiEjvlDotL/X2RcqdCjBEhpiBThjNrMrMbjSz5WbmZnZ41vTPmtkLZrbBzFaa2SVmNiJOm2Jmv4rh683sITN780DFvb+Y2fnxWLyz1HEREREpViXlJeL0r5vZ38ys1cwuGKh497V8x0FkKFABhogUJTNDUIQHgQ8Dq1Km/R44yN3HAfsDs4BPx2m1wJ+Bg4FJwNXAH8ystgdx6KSH+9FrZrY7cDzwaim2LyIiUmoDnJcAWAZ8CfhDD7abU4nyEt0dB5FBTwUYIoOUmZ1tZq+Y2UYze9bM5pvZ0cC5wIlm1mRmT8R5dzSzW81srZktM7MzMtZzQSzt/6WZbQBOM7NhZvZlM3vezBrN7NdmNiktHu7e4u7fd/cHgbaU6c+7++vJ5oB2YI847QV3v9jdX3X3NndfAFQBe3Wz6zVmdkPc77+Y2ayMfVkej8tfgU1mNiJjPzaa2VNm9i8Z859mZg+a2f+Y2Toze9HM3p0xfYaZ3R+XvQuo7/6sAPB/wNlASwHzioiIlMxgyEvE6Ve7+x+BjQXuelnmJfIdB5GhQAUYIoOQme0FfAo4xN3HAkcBy939duCbwA3uXuvuSYL8K2AFsCOhdsA3zWx+xiqPBW4EJgDXEt5qHAe8PS6zDrisF/H9YMzQNBDemvwkx3wHEgowlnWzumOB3xBqbFwH/M7MRmZMPxl4LzDB3VuB54G3AuOBC4FfmtkOGfO/GXiWkKH4LnCFmVmcdh2wOE77OnBqnv08AWhx99u6m09ERKTUBmteokBlm5cQGepUgCEyOLUB1cC+ZjbS3Ze7+/NpM5rZLsA84Gx33+ruS4GfAadkzPaIu//O3dvdfQvwMeAr7r7C3ZuBC4Dje1qV0t2vi9U+3whcDqxOiec44BrgQndf383qFrv7je6+DbgYqAHmZEy/1N1fjvuBu//G3VfGfbsBeA44NGP+l9z9p+7eRmjCsgMw1cx2BQ4BznP3Znd/gFCFNVVs9vJN4LN5DoeIiEg5GHR5iSKUZV5CRFSAITIoufsywg/lC4DXzOx6M9sxx+w7AmvdPbNa5UvAThnfX85aZjfgZjN73cxeB54mZHSm9jLezwFPAj/KDDezUYQE/VF3/1ae1XTE1d3b2f42qMv0uO6PmNnSjH3Zn87VNzvamLr75vhvbVznOnfflDHvS93E60LgGnd/MU/8RURESm6w5SWKVK55CZEhTwUYIoNUfBMxj5BBcOA7yaSsWVcCk8xsbEbYrsArmavLWuZl4N3uPiHjr8bdX6H3RgC7J1/MrBr4XYzPxwpYfpeMZYcBOxP2MeEZ03cDfkqoIlvn7hOAvxPaz+bzKjDRzMZkhO3azfzzgU+b2SozWxXj+WszO7uAbYmIiAy4wZKX6IFyzUuIDHkqwBAZhMxsLzN7R/zxvxXYwvbOnlYD02OCjLu/DDwMfMvMasxsJnA6oX1qLpcDF8VEGzObbGbHdhOfajOriV+r4nYsTvsPM5sS/98XOAe4J34fSWgvuwX4SHwLks/BZvaBWAX1s0Az8GiOeccQMiFr4vb+nfDWJC93fwlYBFxoYVizecD7ullkflz3gfFvJaFApsftfUVERPrLYMlLxLCRcdlhwIi47PBu4laueYluj4PIUKACDJHBqRr4NqEjq1XAFEKP4RA6pQJoNLO/xP9PBqYTflTfDJzv7nd1s/4fALcCd5rZRkKi/uZu5n+WkPHZCbgj/r9bnHYY8Dcz2wTcFv+SuL4FOAY4EnjdQm/nTWb21m62dQtwIqEzsFOAD8Q2rF24+1PA/wKPEDJjBwAPdbPubB8k7Pda4HzgF7lmdPdGd1+V/BEygevcvamI7YmIiAyUwZKXgFBDYkuM41fi/5n9c2Qry7xE1N1xEBn0zD27NpeIiIiIiIiISHlRDQwRERERERERKXsqwBARERERERGRsqcCDBEREREREREpeyrAEBEREREREZGyN6LUESgX9fX1Pn369FJHQ0RE+tALazYB8IbJY0ock6Fj8eLFDe4+udTxKAXlJUREBh/lJUojV35CBRjR9OnTWbRoUamjISIifejEnzwCwA0fm1vimAwdZvZSqeNQKspLiIgMPspLlEau/ISakIiIiIiIiIhI2VMBhoiIiIiIiIiUPTUhERGRQWuH8TWljoKIiIhUMOUlyosKMEREZND6/klv6vR927ZtrFixgq1bt5YoRoNHTU0NO++8MyNHjix1VERERPpNdl4ClJ/oS8XmJ1SAISIiQ8aKFSsYO3Ys06dPx8xKHZ2K5e40NjayYsUKZsyYUeroiIiIDCjlJ/pGT/IT6gNDREQGrQt//yQX/v7Jju9bt26lrq5OmY1eMjPq6ur05klERAa97LwEKD/RV3qSn1ANDBERGbSeWrmhS5gyG31Dx1FERIaCtLwEKB3sK8UeR9XAEBEREREREZGypwIMERGRMrVw4UIefvjhXq2jtra2j2IjIiIilWgw5SfUhERERHL63ZJX+N4dz7Ly9S3sOGEUXzxqL457006ljtaAKfX+L1y4kNraWt7ylrcM2DZFRAarUj/TZegq9bU3mPITqoEhIiKpfrfkFc656W+88voWHHjl9S2cc9Pf+N2SV0odtYK9YfIY3jB5TI+W7c/9P+644zj44IPZb7/9WLBgAQC33347Bx10ELNmzWL+/PksX76cyy+/nEsuuYQDDzyQP/3pT5x22mnceOONHetJ3oY0NTUxf/58DjroIA444ABuueWWXsdRRGQwGQxpmpRGb/ISoPxEX1MNDBERSfW9O55ly7a2TmFbtrXxvTuerZg3Vt/6wMyc0y78/ZM5O+YCWPLP12lpa+8UtmVbG1+68a/86vF/pi6z747jOP99++WN15VXXsmkSZPYsmULhxxyCMceeyxnnHEGDzzwADNmzGDt2rVMmjSJj3/849TW1vKFL3wBgCuuuCJ1fTU1Ndx8882MGzeOhoYG5syZw/vf/351MCYiEg2GNE1Ko7u8BCg/MdBUgCEiIqlWvr6lqPDBJjuzkS+8GJdeeik333wzAC+//DILFizgbW97W8cY6JMmTSpqfe7OueeeywMPPMCwYcN45ZVXWL16NdOmTet1XEVEBoOhnqZJ6Sg/0bdUgCEiIql2nDCKV1IydjtOGFWC2PTMOTf9FUh/e5LvzcZh3743df93mjCKGz42t8dxWrhwIXfffTePPPIIo0eP5vDDD2fWrFk8++yzeZcdMWIE7e0hw+PutLS0AHDttdeyZs0aFi9ezMiRI5k+fXpRY6qLiAx2gyFNk9LoLi8Byk8MNPWBISIiqb541F6MHN65yuCokcP54lF7lShGxXthzSZeWLOpR8t+8ai9GDVyeKewvtj/9evXM3HiREaPHs0zzzzDo48+SnNzM/fffz8vvvgiAGvXrgVg7NixbNy4sWPZ6dOns3jxYgBuueUWtm3b1rHOKVOmMHLkSO677z5eeumlXsVRRGSw+eJRezFiWGWnaVIavclLgPITfU0FGCIikuq4N+3Ee/bfoeP7ThNG8a0PHDBk2gof96ad+NYHDmCnCaMw+m7/jz76aFpbW5k5cybnnXcec+bMYfLkySxYsIAPfOADzJo1ixNPPBGA973vfdx8880dnW6dccYZ3H///Rx66KE89thjjBkTOhX70Ic+xKJFi5g9ezbXXnste++9d293vyKY2XIz+5uZLTWzRTFskpndZWbPxc+JMdzM7FIzW2ZmfzWzgzLWc2qc/zkzOzUj/OC4/mVx2fJpBCwiRTnuTTsxb4+6ju9DLU2T0lF+om+Zu5c6DmVh9uzZvmjRolJHQ0SkrHzrtqf5yQMvcNCuE7jprMNKHZ2infiTRwA6qmg+/fTT7LPPPqWM0qCSdjzNbLG7zx6I7ZvZcmC2uzdkhH0XWOvu3zazLwMT3f1sM3sP8J/Ae4A3Az9w9zeb2SRgETAbcGAxcLC7rzOzx4HPAI8CtwGXuvsfc8VHeQmR8vbZ65fwu6UrecvudVx3xpxSR0cqRHZeApSf6GvF5Cf6rQaGme1iZveZ2dNm9qSZfSaGX2Bmr8S3JUtjhiJZ5pz4luNZMzsqI/zoGLYsZkaS8Blm9lh8Y3KDmVXF8Or4fVmcPr2/9lNEZDBb09QMQENTS4ljIlKwY4Gr4/9XA8dlhP/Cg0eBCWa2A3AUcJe7r3X3dcBdwNFx2jh3f8TD255fZKxLRCpQ46aQlq3eUF5t+kWkcP3ZhKQV+C933weYA3zSzPaN0y5x9wPj320AcdpJwH7A0cCPzGy4mQ0HLgPeDewLnJyxnu/Ede0JrANOj+GnA+vcfQ/gkjifiIgUqTEWXDTGgoxKs++O49h3x3Gljob0HwfuNLPFZnZmDJvq7q8CxM8pMXwn4OWMZVfEsO7CV6SEd2JmZ5rZIjNbtGbNmj7YJRHpL0lh/OoNlZmmSWkoL1Fe+m0UkphpSDIQG83saVIS/gzHAte7ezPwopktAw6N05a5+wsAZnY9cGxc3zuAD8Z5rgYuAH4c13VBDL8R+D8zM1d7GRGRojTEgotNLW1saWljVNXwPEuUl0LGUJeKdpi7rzSzKcBdZvZMN/Om9V/hPQjvHOC+AFgAoQlJ/iiLSKkkaVpTcytNza3UVmtARslPeYnyMiCdeMYmHG8CHotBn4odaF2ZdK5F8W9G6oDX3b01K7zTuuL09XF+EREpQkNTc8dIJA0VWgtDBi93Xxk/XwNuJrz4WB2bfxA/X4uzrwB2yVh8Z2BlnvCdU8JFpAK1tztrN7Ww88QwbKqakYhUpn4vwDCzWuC3wGfdfQOhhsTuwIGEGhr/m8yasnhP34wU9NZE1T5FRHJzdxqbWth9ci1QmQUYn71+CZ+9fkmpoyH9wMzGmNnY5H/gSODvwK1AMpLIqcAt8f9bgY/E0UjmAOtjbdE7gCPNbGJ8qXIkcEecttHM5sTRRz6SsS4RqTCvb9lGW7uzX2wKsHq9CjCkMMpLlJd+LcAws5GEwotr3f0mAHdf7e5t7t4O/JTtzUSKfTPSQOiAa0RWeKd1xenjgbXZ8XP3Be4+291nT548ube7KyIyqKzfso3WdmfvaWOByuzI89X1W3lVmdTBairwoJk9ATwO/MHdbwe+DbzLzJ4D3hW/QxhF5AVgGSH/cRaAu68Fvg78Of59LYYBfAL4WVzmeSDnCCQiUt6Svpz23WE8AKtUA0MKpLxEeenPUUgMuAJ42t0vzgjfIWO2fyG8LYHwZuSkOILIDGBPQobkz8CeccSRKkJHn7fG/izuA46Py2e/ZUnevhwP3Kv+L0REipPUuNhrWnhbVakdeQ52tbWhhszKlSs5/vjju533+9//Pps3by5q/QsXLuSYY47pcfz6i7u/4O6z4t9+7n5RDG909/nuvmf8XBvD3d0/6e67u/sB7r4oY11Xuvse8e/nGeGL3H3/uMynlJcQqVxJIXxHDQx15CnSSaXkJ/qzBsZhwCnAO7KGTP2umf3NzP4KHAF8DsDdnwR+DTwF3A58MtbUaAU+Raji+TTw6zgvwNnA52OHn3WEAhPiZ10M/zzQMfSqiIgUJsns7TWtcpuQ9IlXX4W3vx1WrRqwTba1tRW9zI477siNN97Y7Tw9yXCIiAwGSRq2a91oxtaMUB8YMvCUn+gT/VaA4e4Puru5+8zMIVPd/ZT45mOmu78/GeosLnNRfMuxl7v/MSP8Nnd/Y5x2UUb4C+5+aHxjckIcwQR33xq/7xGnv9Bf+ykiMlglmb2dJoxmbPWIimxC0ie+/nV48MHw2QeWL1/O3nvvzamnnsrMmTM5/vjj2bx5M9OnT+drX/sa8+bN4ze/+Q3PP/88Rx99NAcffDBvfetbeeaZMMDGiy++yNy5cznkkEM477zzOq13//33B0KG5Qtf+AIHHHAAM2fO5Ic//CGXXnopK1eu5IgjjuCII44A4M4772Tu3LkcdNBBnHDCCTQ1NQFw++23s/feezNv3jxuuummPtlvEZFSSmoR1o2pYuq4GlapSYAMNOUn+mS/NXaQiIikatgYMnv1tVXUj62uyBoYB+02sfsZDj+8a9i//RucdRZs3gzz58Pjj0N7O1x+OSxZAmeeCaedBg0NkF3FcuHCguL17LPPcsUVV3DYYYfx0Y9+lB/96EcA1NTU8OCDDwIwf/58Lr/8cvbcc08ee+wxzjrrLO69914+85nP8IlPfIKPfOQjXHbZZanrX7BgAS+++CJLlixhxIgRrF27lkmTJnHxxRdz3333UV9fT0NDA9/4xje4++67GTNmDN/5zne4+OKL+dKXvsQZZ5zBvffeyx577MGJJ55Y0D6JiJSzxk0tDDOYOLqKaeNqWL1RBRhSmLx5CVB+YgDzEyrAEBGRVElmb8LoKurGVFVkAcbZR+/duxW89BIk3R64h+99YJddduGwww4D4MMf/jCXXnopQEfi3tTUxMMPP8wJJ5zQsUxzczj+Dz30EL/97W8BOOWUUzj77LO7rP/uu+/m4x//OCNGhGR+0qRJXeZ59NFHeeqppzri0dLSwty5c3nmmWeYMWMGe+65Z0f8FixY0Cf7LSJSKg1NzUwaU82wYcaUcdU8+nxTqaMkFaLXeQlQfoK+y0+oAENERFIlmb3hw4z62mqWrRmEmb3u3nCsXw/r1nXOcKxbB0cfHb7X1xf8hiRb6Oe66/cxY8YA0N7ezoQJE1i6dGlBy2dz94Lmede73sWvfvWrTuFLly7Nu6yISKVpaGqhvrYKgGnjanhtYzPt7c6wYXreSR9QfmLA8hP9OoyqiIhUrjUbt2f26sdWVeQoJB+/ZjEfv2Zxzxb++tdDVc9MbW190nb1n//8J4888ggAv/rVr5g3b16n6ePGjWPGjBn85je/AULm4IknngDgsMMO4/rrrwfg2muvTV3/kUceyeWXX05raysAa9eGUUHHjh3Lxo0bAZgzZw4PPfQQy5YtA2Dz5s384x//YO+99+bFF1/k+eef74ifiEila2hqpr62GoBp42tobXcaNw3Rvp2kKL3KS4DyE32cn1ABhoiIpGrctD2zVzemmnWbt7GtrT3PUuVl3eYW1m3uYQb1kUegJWvZlhZ4+OFex2ufffbh6quvZubMmaxdu5ZPfOITXea59tprueKKK5g1axb77bcft9wSRgr/wQ9+wGWXXcYhhxzC+vXrU9f/H//xH+y6667MnDmTWbNmcd111wFw5pln8u53v5sjjjiCyZMnc9VVV3HyySczc+ZM5syZwzPPPENNTQ0LFizgve99L/PmzWO33Xbr9f6KiJRaY1MLdbFQfsrYGgCNRCIF6VVeApSf6OP8hGlI82D27Nm+aNGi/DOKiAwRb/3uvRy860S+f9KbuObRlzjvd3/nsXPnM3VcTamjVrATfxLeStzwsbkAPP300+yzzz6ljBLLly/nmGOO4e9//3tJ49EX0o6nmS1299klilJJKS8hUr72++rtnHTorpx3zL4sffl1jrvsIX72kdm8c9+ppY6alLnsvAQoP9HXislPqAaGiIikatjY0lEDY3J8a1WJHXmKiMjQtqWljU0tbR01MKbFgniNRCJSeVSAISIiXWxuaWXLtjbqkiYk8bOhSe2Fe2v69OmD4m2JiEilSArf68eEtKy+tophBqvXqwBDKtdQzU9oFBIREemiYWMoqOjoxDMpwNhYWTUwDtujvktYIT1qS35qgioilaKjAGNsSNNGDB9GfW01q9QHhhQgLS8Byk/0lWLzEyrAEBGRLtZ0ZPaSGhgh09e4qbIKMD49f89O32tqamhsbKSurk6Zjl5wdxobG6mpqZz+UERk6GqMtQfrYg0MCCORrN5QWWmalEZ2XgKUn+grPclPqABDRES6aMyqbju2egRVI4ZVfBOSnXfemRUrVrBmzZpSR6Xi1dTUsPPOO5c6GiIieSWF70mhPISRSFas21yqKEmFU36i7xSbn1ABhoiIdJEUVCTVbc2MybXVFdeJ56lXPg7A1R89FICRI0cyY8aMUkZJREQGWENHDYyqjrBp46tZ9NLaUkVJKkh2XgKUnyglFWCIiEgXSUFFZnXbutqqiquBsXVbW6mjICIiJdbQ1Ext9QhqRg7vCJs2robXN29j67a2TuEi2ZSXKC8ahURERLpoaGpm/KiRVI3YnkzU11ZXXCeeIiIiDU0tHZ1SJ6bEoVRfUz8YIhVFBRgiItJFY1NLR8edifraqorrxFNERKSxqbljOPDEtFiAoZFIRCqLCjBERKSLNU3NHUOnJupqq2lsaqG9XcNniohI5WhsaunU/wWEUUgAVqsAQ6SiqA8MERHpoqGpmX2mjesUVl9bTWu7s37LNiZmZQTL1fx9ppQ6CiIiUmINTc0cPH1ip7CpY1WAIYVRXqK8qABDRES6yNWEBMJwdJVSgHHm23YvdRRERKSE2tqdtZtbqM9Kt8aNGkHNyGGsWq8CDOme8hLlRU1IRESkk5bWdtZv2dalCUnyfc3GyhqJREREhq61m1pwh/qxndM0M2PauBpWq3NqkYqiAgwREekk6agzVwFGMsRqJTjxJ49w4k8eKXU0RESkRJI0LXNY8MSUcTWsVg0MyUN5ifKiAgwREemksSnUsMjZhKSCCjBERGRoy5WmAbEGhgowRCqJCjBERKSTNU3pNTAmjK5imEFDk5qQiIhIZWjIkaZBGIlk1fqtuGt0LZFKoQIMERHppCG2B56cldkbPsyYNKa6opqQiIjI0JYUuten1MCYMraa5tjvk4hUBhVgiIhIJ42bcle3ra+tUg0MERGpGA1NzYwYZowfNbLLtGnjk6FUVTAvUik0jKqIiHTSsLGZUSOHM6a6axJRX1tZNTCOmblDqaMgIiIl1NjUTF1tFWbWZdq0caEAY9WGrew1bexAR00qhPIS5UUFGCIi0klDUzP1Y7vWvoBQA2N546YBjlHPnTJ3eqmjICIiJdTY1JI6AgnA1FiAoZFIpDvKS5QXNSEREZFOGjflzuzV11Z39OheCba0tLGlpa3U0RARkRIJhfLpadqUcSF89QYVYEhuykuUFxVgiIhIJ2s2Nqf21g5QV1vNlm1tbGpuHeBY9cxpP3+c037+eKmjISIiJdLQ1EL9mPRahdUjhjNpTBWrVIAh3VBeoryoAENERDppaGphcjdNSICKqoUhIiJDk7t3WwMDwkgkqoEhUjlUgCEiIh3a2521m5q7bUICsKaCOvIUEZGhaVNLG82t7dTlqIEBYSQSjUIiUjlUgCEiIh3WbW6h3bfXtMiWFGBU0kgkIiIyNDXGtKouR7NICCORqAmJSOVQAYaIiHRoiE1DclW3TUYnURMSEREpd0lhe65CeYAp42poaGpmW1v7QEVLRHpBw6iKiEiHjrdVOZqQTIrVcCulBsbxB+9c6iiIiEiJdBTK56mB4R7StR3GjxqoqEkFUV6ivKgAQ0REOiR9W+TqxLN6xHDG1YyomAKME2bvUuooiIhIiTQWUoAxPkxbtX6rCjAklfIS5UVNSEREpEMhb6vqx1ZXTBOStZtaWLupMuIqIiJ9Kylsn9RNJ55TxtYAaCQSyUl5ifKiGhgiItKhsamZEcOMcTUjc85TP6a6YkYh+cQvFwNww8fmljgmIiIy0BqbmhlXM4KqEbnf2U4bnxRgVEa6JgNPeYnyohoYIiLSoaGpmbraKoYNs5zz1I+tqpgmJCIiMnQ1NLXk7JQ6MWl0FSOHm0YiEakQKsAQEZEODU0t3TYfgdC8pFKakIiIyNDV0NRMfY5OqRPDhhlTxtawer0KMEQqgQowRESkQ2NTM3V5CjDqxlSzfss2Wlo15JyIiJSvxk0tHcN/d2fquGpWb1QBhkglUAGGiIh0CDUwus/sJZnBxk1qRiIiIuWroak557DgmaaOq2GVamCIVAR14ikiIgC4O2uamplcQBMSCMPTlfuQcx+es1upoyAiIiWwra2d1zdvoy5PoTyEAow/PdcwALGSSqS8RHlRAYaIiADQ1NxKS2t73sxeUkOjEkYied+sHUsdBRERKYFk2Mt8/TpBGImkqbmVpuZWaqv180g6U16ivKgJiYiIAKH5COTP7CXTGzaWfwHGyte3sPL1LaWOhoiIDLBktKx8zSIh9IEBsFojkUgK5SXKiwowREQEyMzsFdiEZFP5j0TyuRuW8rkblpY6GiIiMsAaCyyUh9CEBNBIJJJKeYny0m8FGGa2i5ndZ2ZPm9mTZvaZGD7JzO4ys+fi58QYbmZ2qZktM7O/mtlBGes6Nc7/nJmdmhF+sJn9LS5zqZlZd9sQEZHcGmMBRr4mJKOrhlMzclhF1MAQEZGhqaEjTSugCUlSgKGRSETKXn/WwGgF/svd9wHmAJ80s32BLwP3uPuewD3xO8C7gT3j35nAjyEURgDnA28GDgXOzyiQ+HGcN1nu6BieaxsiIpLDmvi2Kl8nnmZGfW11R+ZQRESk3CQ1MArtxBNg1XqlayLlrt8KMNz9VXf/S/x/I/A0sBNwLHB1nO1q4Lj4/7HALzx4FJhgZjsARwF3uftad18H3AUcHaeNc/dH3N2BX2StK20bIiKSQ1KjYtKY/Jm9+trqimhCIiIiQ1NDUzNVI4YxtoBOOcdUj2Bs9Qj1gSFSAQakDwwzmw68CXgMmOrur0Io5ACmxNl2Al7OWGxFDOsufEVKON1sQ0REcmjc1MzE0SMZMTx/0lBfW8UaNSEREZEy1dDUQv2YKmIL87ymjq9RAYZIBej3AgwzqwV+C3zW3Td0N2tKmPcgvJi4nWlmi8xs0Zo1a4pZVERk0GnY2FJQZ2dQOTUwznjrGzjjrW8odTSkH5nZcDNbYmb/L36fYWaPxX6wbjCzqhheHb8vi9OnZ6zjnBj+rJkdlRF+dAxbZmZqjipSQRo3NVM/trA0DcJIJKtUgCEplJcoL/1agGFmIwmFF9e6+00xeHVs/kH8fC2GrwB2yVh8Z2BlrqTb0gAAIABJREFUnvCdU8K720Yn7r7A3We7++zJkyf3bCdFRAaJhqbmggsw6mqrWLuphfb2osqNB9w7953KO/edWupoSP/6DKGZauI7wCWxH6x1wOkx/HRgnbvvAVwS5yP2z3USsB+hL60fxUKR4cBlhD669gVOjvOKSAVoaGqmroAmkYmp42o0ComkUl6ivPTnKCQGXAE87e4XZ0y6FUhGEjkVuCUj/CNxNJI5wPrY/OMO4Egzmxg77zwSuCNO22hmc+K2PpK1rrRtiIhIDo2bWgrq7AxCDYy2dmfd5vKuhfH8miaeX9NU6mhIPzGznYH3Aj+L3w14B3BjnCW7r62kf6wbgflx/mOB69292d1fBJYROg0/FFjm7i+4ewtwfZxXRCpAY1NLQSOQJKaNq+G1jc1lXzAvA095ifKSv1ebnjsMOAX4m5klA+eeC3wb+LWZnQ78EzghTrsNeA8h47AZ+HcAd19rZl8H/hzn+5q7r43/fwK4ChgF/DH+0c02REQkh4aNhdfASOYLhR6FZxAH2rk3/Q2AGz42t8QxkX7yfeBLwNj4vQ543d1b4/fM/rE6+tRy91YzWx/n3wl4NGOdmctk98H15r7eARHpe+5OY1PhzSIh1MBobXcaN7UwuYimJzL4KS9RXvqtAMPdHyS9nwqA+SnzO/DJHOu6ErgyJXwRsH9KeGPaNkREJN3WbW1sbG4tONOW1NRo2NjMG6eOzTO3SN8zs2OA19x9sZkdngSnzOp5puUKT6ul2uXVrJmdSRjSnV133TVPrEVkIGzY2kpLWzv1BdYqhO1Dqa7esFUFGCJlbEBGIRERkfKWdMhZaHvhyfGt1pomjUQiJXMY8H4zW05o3vEOQo2MCWaWvKDJ7B+ro0+tOH08sJbi++DqRP1piZSfxpg2FVMDY9r47QUYIlK+VIAhIiI0bCwus9fRhKSpvPvAkMHL3c9x953dfTqhE8573f1DwH3A8XG27L62kv6xjo/zeww/KY5SMgPYE3ic0HR1zziqSVXcxq0DsGsi0ksNMW0qtF8nCKOQABqJRKTM9WcfGCIiUiEakrdVBVabHT9qJMOHWcdyImXkbOB6M/sGsITQoTjx8xozW0aoeXESgLs/aWa/Bp4CWoFPunsbgJl9itCZ+HDgSnd/ckD3RER6JKmBUTem8BoYk2urGWZoJBKRMqcCDBER6ahJUWh74WHDjLoxVWVfgPGf79iz1FGQAeDuC4GF8f8XCCOIZM+zlRyderv7RcBFKeG3EToZF5EKsr1QvvAaGCOGD6O+tprVG8o7XZOBp7xEeVEBhoiIdPRlUUx74fra6rJvQjJvz/pSR0FERAZY0oRk0ujCCzAgdOSpJiSSTXmJ8qI+MEREhIamZmqrR1AzcnjBy9TVln8NjCdXrufJletLHQ0RERlAjZuamTh6JCOGF/dTZ+q4GnXiKV0oL1FeVIAhIiI0NLUUNdwchPbCDWVeA+Nrv3+Kr/3+qVJHQ0REBlDDxpaiahQmpo2vVgGGdKG8RHlRAYaIiNDY1ExdkZm9+rHVNDQ1EwZyEBERKQ+Nm5qLGoEkMXVsDes2b2PrtrZ+iJWI9AUVYIiICA1NzUXXwKgbU0VzaztNza39FCsREZHiNTb1rAbG1PE1ALymjjxFypYKMEREJDYhKbIGRpy/3JuRiIjI0LKmqblnTUjGhQKM1RvVjESkXKkAQ0RkiGtta2fd5pYeNSGB0PxERESkHDS3trFxayt1Y3rQhCQWYKxarwIMkXKlYVRFRIa4tZtbcIfJPWhCApT1SCRfOnqvUkdBREQGUDK8d1LIXoyOGhjqyFMyKC9RXlSAISIyxDVsjJm9ImtgTB5b/k1IDt5tUqmjICIiAygpwOhJDYxxo0ZQM3KYCjCkE+UlyouakIiIDHGNm0INimKbkEyqgBoYi19ay+KX1pY6GiIiMkAaYprWkxoYZsbUcTWsUieekkF5ifKiAgwRkSEuKYAodhSSkcOHMWH0yLIuwPju7c/y3dufLXU0RERkgDRsjGnamOILMCD0g7FafWBIBuUlyosKMEREhriOJiQ9eFtVX1vdUV1XRESk1Bo3xSYkRRbKJ6aNq9EoJCJlTAUYIiJDXMOmZqqGD2NsdfHdItXXVpV1DQwRERlaGjY2M2rkcMb0IE0DmDqumlXrt+LufRwzEekLKsAQERniGja2UF9bhZkVvWxdbXVZd+IpIiJDS+Omlh7XvoDQhKS5tZ31W7b1YaxEpK+oAENEZIhraGruUfMRgMm11aqBISIiZaOhqbnoUbUyTRufDKWqtE2kHGkYVRGRIa5xUzOTe5jZqxtTxcatrWzd1kbNyOF9HLPe++r79i11FEREZAA1NLWw04SaHi8/dVxYdtWGrew1bWxfRUsqmPIS5UUFGCIiQ1zDxhb2mTauR8smNTcaN7Ww04RRfRmtPrHfjuNLHQURERlAjU3NzNyp58/+abEAQyORSEJ5ifKiJiQiIkOYu9O4qedNSJJquo1l2ozkwecaePC5hlJHQ0REBkB7u9O4qYX6sT3vA2PKuJCurd6gAgwJlJcoL6qBISIyhG3Y0sq2NqduTM8ye0lHaeXaD8YP730OgHl71pc4JiIi0t/Wb9lGW7tTN6bnfWBUjxjOxNEjWaUCDImUlygvqoEhIjKErfn/7N15eFvnfeD77wuABAjuBLhoISVZoiTbcbzJqyQvcmI5aZO4bbamTdxObj3TyUyny3Ubz3NnMk2mbfq4M52baSe3ztY0bZrFdRwnTaK6sSyL3hQpsq04Fk1KpHYuADcAJAEQeO8fOIeiKIoEybOR+H2eRw/Jw4NzXiUyz8sffosReGhcRhNPKJShCCGEEG6KpwrPtKVmFZqaa0KSgSGER0kGhhBClDAzc2KpHdunMzBS3szAEEIIYZ2njp7jsX2dnB+ZYG1dBY/s3caDN65ze1nTBo1genSJWYWmQgBDnmtCeJEEMIQQooTFk4XNnhmIWKxweYBwuV8yMIQQYpV76ug5Hn3yGBPZHADnRiZ49MljAJ4JYpgZGJFljFGFQiPPn18Ys2JJQgiLSQmJEEKUsOVmYJiv9WoPDCGEENZ4bF/ndPDCNJHN8di+TpdWdLlYwnymLTMDozZELJkmm8tbsSwhhIUkA0MIIUpYLJnGp6A+vPTNXrSqfPpdL6/501++zu0lCCHEqnB+ZGJRx90QT2XwKahbxjMNoLkmiNaFZ+SaWu+NCBfOkr2Et0gAQwghSlgsmaGhshy/Ty35GpGqIKfj4xauyjqbG6vcXoIQQqwKa+sqODdHsGJtnXd+wS8804LLeqZBoYQEoG90UgIYQvYSHiMlJEIIUcJiyfSyykegUELi1QyMf/15P//68363lyGEECveI3u3UVHmv+RYRZmfR/Zuc2lFlys805aXfQGFJp6ATCIRgOwlvEYyMIQQooRZEcBorCpnKJUhl9fLftfLal84eBKAd1zT7PJKhBBiZXvwxnVkc3keeeJ1AGorAvzxe9/mmQaeAPFkeslNqWe6GMDwZnBeOEv2Et4iGRhCCFHC4snMsjd7kaogeQ1DKZlEIoQQq9kNrXXTn//KTa2eCl5AoYRkuUF5gEhlOWV+RZ9kYAjhORLAEEKIEmZVCQng2TISIYQQ1uiJpQAI+BSnh1Iur+Zy8WSaSOXyAxg+n6KpOkT/qAQwhPAaCWAIIUSJGs9MMZ7JWRDAKGRwxBKSgSGEEKtZb7wQtLhpQz2nh7zVvHkikyOVyRGtXn4JCUBTTZD+hAQwhPAaCWAIIUSJiicLAQcrSkigkM0hhBBi9eqJjVMfLuPt62o5PTSO1trtJU0zn0FRCzIwoDCJpE8yMITwHGniKYQQJWrQ2Ow1LruJp3cDGH/5oRvcXoIQQqwavbEUG6OVtEXCTGbzDCbSNBkNL90WT1kTlDc114Q42BWz5FpiZZO9hLdIBoYQQpSoWMJ4t2qZAYyaigBlfkUs6b0SkrV1Faytq3B7GUIIsSr0xlNsilTS2hAG8FQZSTxpzTPN1FwTIpmeIpmesuR6YuWSvYS3SABDCCFKlFXvVimliFQGPZmB8b3XzvO91867vQwhhFjxJjI5LoxOsjFayQYjgHEq7p0AhvkMsioDo6W2EAjpl0kkJU/2Et4iJSRCCFGizAwMKzZ70ery6Xe/vOTvXz4FwHuuX+vySoQQYmU7ZUwd2RitZF19BUp5KwPDzAK0MgMDoH90ks2NVZZcU6xMspeY21NHz/HYvk7Oj0ywtq6CR/Zuc2S0sgQwhBCiRMWSaWpCAYIB/7KvVcjA8F4JiRBCCGv0GiNUN0UqCQb8rK2t4IynAhhpqoIBQmXLf6bBjACGTCIR4jJPHT3Ho08eYyKbA+DcyASPPnkMwPYghpSQCCFEiYqlMpa9UxWt8mYJiRBCCGv0xArBio3RQvlIa0MFpzwUwIgnM5aVj0BhCglA36g824SY7bF9ndPBC9NENsdj+zptv7cEMIQQokTFEmnrAhjV5cSTGU+N1BNCCGGd3liKaFU51aEyANoawp4qIYmnrHumAVQGA1QHA9IDQ4g5nB+ZWNRxK0kAQwghSlQsmSZabc27VdHKIJlcnrFJ6dYuhBCrUU88xcZI5fTXGyKVDCbSTGRy87zKObFEhkildRkYAE01QQlgCDGHK01lcWJai/TAEEKIEhW3soTECITEkmlqK8osuaYVPv/rN7u9BCGEWBV6Yynu2to4/fXMUarbWqrdWta0eCrNzRvrLb1mS22IPglglDzZS1zukb3bLumBAVBR5ueRvdtsv7dtGRhKqS8rpQaUUj+bcey/KaXOKaVeNf68e8b3HlVKdSulOpVSe2ccf8A41q2U+uSM45uUUq8opbqUUt9USpUbx4PG193G9zfa9XcUQoiVKpvLMzKeJVJpXQ8MKNQge0lDZTkNFr8jJ4QQpSaVnmIgkWZT9GIGRtuMAIbbcnnNUCpD1OKf9801IQbGpAdGqZO9xOUevHEdn3nftdNfr6ur4M9++TpHppDYWULyt8ADcxz/S631DcafHwAopa4BPgxca7zm/yil/EopP/DXwLuAa4BfNc4F+HPjWu3AMPBx4/jHgWGt9RbgL43zhBBCzGAGGqwqITEDIV5r5Pntw2f49uEzbi9DCCFWtN64MUJ1ZgmJEcA4ZXzPTcPjGfIaIhb2wIBCAKN/bJJ8Xvo7lTLZS8zt2nW1AHzuV2/khU/ucSR4AUUEMJRSH1BKVRuf/z9KqSeVUjct9Dqt9fPAUJHreB/wDa11WmvdA3QDtxp/urXWJ7XWGeAbwPuUUgrYAzxhvP6rwIMzrvVV4/MngPuM84UQQhjMQIMdJSRe8sSRszxx5KzbyxDzWOo+QwjhnFPxSyeQANSFy6gOBjwxSnU6KG9xAKOlJsRUXhNPeSu7UDhL9hJz6+xLALDd4RKyYjIw/ovWOqGU2gXspRAc+Pwy7vkflFKvGyUmZqHaOmBmWOuscexKxyPAiNZ6atbxS65lfH/UOF8IIYThYgDDmgyMhnA5SkHMYyUkYkWwep8hhLBYT6yQZbFhRgaGUoq2iDcmkZjPNCvHqEIhAwOQRp5CzOF4X4Iyv7qktMwJxQQwzM4cvwB8Xmv9XWCpPx0+D2wGbgAuAP/DOD5XhoRewvH5rnUZpdTDSqnDSqnDg4OD861bCCFWlZjF71YF/D7qw+Wey8AQK4KV+wwhhA16Yykaq4NUBS/t/9/WEOaUhwIYVgXlTc01hWekBDCEuFxn3xibG6so8zs72LSYu51TSv0N8EHgB0qpYJGvu4zWul9rndNa54EvUCgRgUIGReuMU9cD5+c5HgPqlFKBWccvuZbx/VquUMqitX5ca71Da72jsbFxrlOEEGJViltcQlK4Vvn0dYVYBMv2GUIIe/TGU2yKXP4ua1tDmLNDE673iLA6KG9qqS1kYMgkEiEu19mXcLx8BIrbIHwQ2Ac8oLUeARqAR5ZyM6XUmhlf/hJgTih5GviwMUFkE9AOHAJ+ArQbE0fKKTT6fFprrYH9wPuN1z8EfHfGtR4yPn8/8KxxvhBCCEMsmSZU5iNc7rfsmtGqoJSQiKWwbJ8hhLBHT2z8kv4XprZImEwuT3/C3V/w48k0AZ+iJmTtGO/GqiBKQb9MIhHiEqMTWc6PTrKtpcbxewcWOkFrPa6UGgB2AV3AlPFxXkqpfwTuAaJKqbPAp4B7lFI3UCjp6AX+rXGPN5RS3wJ+blz/E1rrnHGd/0BhY+MHvqy1fsO4xR8B31BK/XfgKPAl4/iXgK8ppbopZF58eKG1CiFEqYklM0SrgljZ4zhSFeT1syOWXc8Kf/ubty58knDVUvcZQghnJCazxJJpNs5R5942PYlknDW1FU4vbVo8mSFSVY7PZ23f/oDfR7QqSP+oZGCUMtlLXO6tfncaeEIRAQyl1KeAHcA24CtAGfD3wM75Xqe1/tU5Dn9pjmPm+X8C/Mkcx38A/GCO4ye5WIIy8/gk8IH51iaEEKUulkxbnmpbKCHxVgZGhYUZJsIeS91nCCGcYU4guVIJCcDpoXFuv8q9nvmxZHp6nLfVWmpCUkJS4mQvcbnjxgSSbR4tIfkl4L1ACkBrfR5wfqVCCCEsU8jAsLbZWbQqSDI9xWQ2t/DJDvnaS7187aVel1chFiD7DCE8zJxAMlcGxtq6Cvw+xem4u408Y6mM5RNITM01IWniWeJkL3G54xfGqA4FWGP0iXFSMQGMjNFDQgMopZydkyKEEMJydmVgAAwmvFMr/P3XL/D91y+4vQwxP9lnCOFhvWYAY44MjDK/j7V1IddHqcYSaRotfqaZmmuCEsAocbKXuJzZwNPKUuRiFRPA+JbRHbxOKfVbwL9SmCAihBBiBcrnNUOpjA0BjML14ilvlZEIz1vSPkMpFVJKHVJKvaaUekMp9cfG8U1KqVeUUl1KqW8aTcAxGoV/UynVbXx/44xrPWoc71RK7Z1x/AHjWLdS6pMW/72FWBF64ilaakJXTKPf0FDpagBDa008lbYtA6OlJsTweNZT2YVCuElrTWd/wpXyESgigKG1/gvgCeCfKNSn/let9f+2e2FCCCHsMTKRJZfXlm/2zABGzEMZGML7lrHPSAN7tNbXAzcADyilbgf+HPhLrXU7MAx83Dj/48Cw1noL8JfGeSilrqHQ8Pta4AHg/yil/EopP/DXwLuAa4BfNc4VoqT0xlJzTiAxtTaEXQ1gjGdyTGbzlgflTc1GiryXsguFcNP50UkSk1OuTCCBIpp4AmitnwGesXktQgghHBBLFjZhVm/2zICIeX0hirWUfYZRdpI0viwz/mhgD/AR4/hXgf8GfB54n/E5FAImf6UKua/vA76htU4DPcYUM7NJeLfRNByl1DeMc3++yL+eECtab3ycvdc2X/H7bQ1hhlIZEpNZqi0eY1oM85kTsa2EpBDA6BubpLXhyoEcIUpFZ98Y4M4EEpgngKGUSmDUo87+FoV9gzshFyGEEMtiZkhICYlwkxX7DCNL4giwhUK2xAlgRGs9ZZxyFlhnfL4OOEPh4lNKqVEgYhx/ecZlZ77mzKzjty38NxNi9RidyDKUyszZ/8K0IVL4pf7M0ATXrHUjgFF45thZQgLQJ6NUhQAuTiDZ2uyxAIbWWjqACyHEKhQzAgxWTyEJlfmpCgY8lWb7zX97h9tLEFdgxT5Da50DblBK1QHfAa6e6zTj41ydxvQ8x+cqs70s4KKUehh4GKCtra2IVQuxcvTOM4HEdHGUaopr1jr//qaZgWFXE08zgCGNPEuX7CUu1dmXYG1tiNoK5wOWUGQJCYBSqgmYnpOitT5ty4qEEELYyq4MjMI1y6WERCzJcvYZWusRpdRzwO0UmoEGjCyM9cB547SzQCtwVikVAGqBoRnHTTNfc6XjM+/9OPA4wI4dO+bKKBFixeqNFwIYm+YJYLROBzDc6YMRtzkDo6YiQDDgkwCGEIbOPvcaeEIRTTyVUu9VSnUBPcABoBf4oc3rEkIIYZNYMk3Ap2yJnEergtObSS94/PkTPP78CbeXIeax1H2GUqrRyLxAKVUBvAN4E9gPvN847SHgu8bnTxtfY3z/WaOPxtPAh40pJZuAduAQ8BOg3ZhqUk6h0efTy/zrCrGi9MRSKHUxy2IutRVl1IXLOBV3K4Bh9MCotCcDQylFS22IvjEJzpcq2UtclM3lOTGYdK2BJxQ3RvUzFN7ReEtrvQm4D3jB1lUJIYSwTTyZoaGyHJ/P+tndEY9lYPz4zQF+/OaA28sQ81vqPmMNsF8p9TqFYMMzWuvvA38E/L7RjDMCfMk4/0tAxDj++8AnAbTWbwDfotCc80fAJ7TWOSOD4z8A+ygERr5lnCtEyeiNpVhbW0GobO4RqqY2FyeRxJJpakIBygPF/FqzNM01IcnAKGGyl7jo5GCKbE671sATiishyWqt40opn1LKp7Xer5T6c9tXJoQQwhaxZNq2cXPRqiCHeoZsubZYtZa0z9Bavw7cOMfxk1ycIjLz+CTwgStc60+AP5nj+A+AHxTxdxBiVeqJj887QtXU2hDmjXOjDqzocrFUxrZnmqm5JsTrZ0dsvYcQK8FxYwKJp0tIgBGlVBXwPPAPSqn/F5ha4DVCCCE8KpZME622L4AxPJ5lKpe35fpiVZJ9hhAedSqeYsM8E0hMGxrCnB2ecOVnf9zGoLyppSZI3+gkhaozIUpXZ1+CgE+xubHKtTUUE8B4HzAO/B6F1MoTwHvsXJQQQgj7xJIZopX2NDszJ5sMyShVUTzZZwjhQSPjGUbGs2wqIoDR1hBmKq+54MKo0VgyY1sDT1NzTYj0VJ6xCYmtitLW2ZfgqsZKW0u2FlL0nY1a0JcoNNcas2tBQggh7KO1tj0DAwobSi8IlfkXrN0W3iD7DCG8paeIEaqmtkihzOSMC30wnMjAaDZGqfZJH4ySJHuJi473JVxt4AnF9cB4HtitlKoHfgwcBj4E/JqdCxNCCGG9ZHqK9FR+OlPCamZgxCuNPL/6by5rhSC8R/YZQnjQxRGqC/fAMKeUnBoa505bV3WpbC7P8HjW9gyMltqLAQw3a/+FO2QvUZCYzHJuZIKP3Nbm6jqKycBQWutx4JeB/621/iXgGnuXJYQQwg7miFO7xs1FjNIUrwQwxIog+wwhPKgnNo5PFRp0LmRNbQVlfuX4JJJho1wxYncGRnUhgCGTSEQpe6s/AcC2ZneDeMVkYCil1B0U3gn5+CJeJ4QQwmPMwIJtJSTGdeMeKSH53I+7APid+9pdXomYh+wzhPCg3liKtXUVBAMLp877fYr19c6PUjXLFRttzsBoqik82/pd6PFheuroOR7b18n5kQnW1lXwyN5tPHjjOtfWU0pkL1FwvM8IYLichVRMBsZ/Ah4FvqO1fkMpdRWw395lCSGEsMN0AMOmzV51MEB5wOeZDIwXumO80B1zexlifrLPEMKDeuMpNhXR/8LU2hDmdNzpAEbhWWN3BkaozE99uMy1HhhPHT3Ho08e49zIBBo4NzLBo08e46mj51xZT6mRvURBZ1+CqmCA9fUVrq5jwXc4tNbPU6hPNb8+CfyOnYsSQghhD/PdKrsanimliFaWM+iRAIbwPtlnCOE9Wmt6YikevKH4d/jbGip47cyIjau6XDxlBuXtDWBAoZFn/5g7z7bH9nUykc1dcmwim+OxfZ2ShSEcU2jgWY1SytV1uDf/RAghhOPMd6sabBqjCoUyEq+UkAghhFi8oVSGxORUURNITBsaKhmdyDI6nrVxZZeKJcweGPaWkIAZwHAnA+P8yMSijgthNa01nUYAw20SwBBCiBISS6apD5dR5rfvx3+0KuiZEhIhhBCLt5gJJCaz2aeTfTBiqTTlfh/VQfvb5rTUhFwrIVlbN3fK/pWOC2G1/rE0oxNZtq+EAIZSamcxx4QQQnhfPJmxvVY4UlnumQBGfbic+rD978yJpZN9hhDe0xMrBCE2RhaRgRFxPoART2aIVpU7ktLeXFMIzk/l8rbfa7ZH9m7DP+uvWFHm55G92xxfSymSvQS82TcGuD+BBIrr8v2/gZuKOCaEEMLjYsm0bQ08TWYJidba9TrJ/++jN7t6f1EU2WcI4TG9sRR+nypqhKrJPPfUUMquZV0mlkzbHpQ3NdeG0BoGk2nW1Dqb+fDe69fyn598nTwwmc1TFQzw3x98m/S/cIjsJQoNPAG2t9S4vJJ5AhjGSLM7gUal1O/P+FYNsPA8JSGEEJ4TS2a4dq29D59oVZCpvGZ0Iktdib9jIa5M9hlCeFdPPMX6+opFlRtWBQNEKss540IGhhNaakIA9I1OOh7AeOP8GOPZPH/5oev5xqEzJNNTErwQjursS9BSE6I2XOb2UuYtISkHqigEOapn/BkD3m//0oQQQlitkIFh77tV5mbSC2Ukf/6j4/z5j467vQwxN9lnCOFRvbHUospHTG2RsLM9MJzMwDACGG5MIukwRnju3BJld3uUN86PEffAM7ZUyF7i4gQSL7hiBobW+gBwQCn1t1rrUw6uSQghhA0mszkSk1P2l5AYm8lYMsOWJltvtaCfnhp2dwHiimSfIYQ3aa3pjaW4ZWPDol/b1hDmiEM/d7XWRl8nZzIwLgYwnG/k2dE9yPaWapqqQ+xqb+Qv/uUtXjgR573Xr3V8LaWo1PcS2VyeEwNJ7mqPur0UYP4Skv+ltf5d4K+UUnr297XW77V1ZUIIISwVTxXGzdmdgRHxUAaG8C7ZZwjhTYPJNKlMjo2R4vtfmNoawnzvtfNkc3lbp10BJNJTZHJ5Gh3KwIhUlhPwKccnkUxmc/ykd5iP3r4BgOvW1VJbUUZH16AEMIQjemMpMrm89zMwgK8ZH//CiYUIIYSwl5luane67XQGRkICGGJess8QwoN6jQkkG6JLKCFpCJPXcG54go1LeP1imM8YpzIwfD5FU3XQ8QyMQz1DZKby7DLe/fb7FHdujtDRFfN9kJWoAAAgAElEQVREs2yx+h03GnjOGcC4cAE+/GH45jehpcWR9cxXQnLE+HjAkZUIIYSwlZkRYXcJSX24HJ+6mPEhxFxknyGEN/XGC1NENi2lB0bDxVGqdgcwnMoqnKm5NuR4AOOF7hjlfh+3bbpY0rOrPcoPf9bHicEUW5qqHF2PKD2dfQn8PjX3v7XPfAY6Ogof//qvHVnPgrldSqmdSqlnlFJvKaVOKqV6lFInnVicEEII68QSzmz2/D5FQ2W5J0pI1tSGWFMbcnsZYh6yzxDCW3pjKQI+xfr6xU/a2GAEPZxo5DmdgVHpXACjpSZE36izAYyDXTFu2lBHuPzi+867tzQC0NE16OhaSlWp7yWO9yXYFK0kGJg1IOzCBfjylyGfh698Bfr6HFnPfCUkpi8BvwccAXL2LkcIIYRdYikzA8P+zV60Kkgs6X4Gxv/68I1uL0EsTPYZQnhIbzxFa0OYwBJ6WDRVBykP+JwJYExnYDg3rru5JkRHV8yx+8WSaX5+YYz/+/6tlxxvi4RpawjT0R3jN3Zucmw9parU9xKd/WO8fX3d5d/4zGcgmy18nss5loVRzE+mUa31D7XWA1rruPnH9pUJIYSwVCyRobLcT0W5f+GTl6kQwHA/A0OsCLLPEMJDemLjS2rgCYU+Ea31FZyO2x/AMPs6NVQ6G8BIpKdIpaccud+LJwo/Cne1N172vV3tUV4+OUQ2l3dkLaI0JdNTnBmaYHvzrP4XFy4Usi7yxr+/TMaxLIxiAhj7lVKPKaXuUErdZP6xfWVCCCEsFUumiVY71K29yhslJH/8vTf44++94fYyxPxknyGER2itORVPLat/xYZIpTMZGMk09eGyJWWKLFVLbeEZ6tQkko6uQWoryrhuXe1l39u9JUoyPcWrZ0YcWUspK+W9xFv9V2jg+ZnPXAxemMwsDJsVU0Jym/Fxx4xjGthj/XKEEELYJZ5KO9bsLFoVJO6BEpKfnx9zewliYbLPEMIjBhJpxjM5Ni0jgNHWEOZQz5DtEzLiyYyjDTwBmqsLfRD6xybZ3Ghv80ytNR1dMe7cHMHvu/x/xzs3R/GpQo+MWzY2zHEFYZVS3kt0GhNItrfUXPqNl14qZF3MlMnAiy/avqYFAxha63ttX4UQQgjbxRIZNiwxLXixolVBxjM5xjNTlzQeE2I22WcI4R09scIEko1LmEBiam0Ik0xPMTyetbW8I5ZMOzZC1dRcezGAYbeTsRTnRyf59/dG5/x+bbiM69bX0dE1yO+/c+uc5wixXJ19CcLl/sub+h496s6CKCKAoZT6r3Md11p/2vrlCCGEsEssmebmjfWO3MvcVMYSGdoiEsAQVyb7DCG8o9cIYCwnA2ODMUr1VDxlawAjnsxw9dqahU+0UHNNIYDRN2p/ieQL3YVmobvb5w5gQKGM5PMHTjA2maUmVGb7mkTpOd43xtbmanxzZAHxrnfBu98N//E/OrqmYorGUjP+5IB3ARttXJMQQgiL5fKaoXHn0m0bjfuYk0+EmIfsM4TwiJ54inK/j7V1ix+hamozMv3s7oMRS6annzVOqQoGqAoGHMnAONgVo7WhYno07Vx2tUfJ5TUvnZC+x8J6Wms6+xJsn93/AgrNOn/0I5h0dqwwFFdC8j9mfq2U+gvgadtWJIQQwnJDqQxaOzduzgyUxBLuBjCualz6u4jCGbLPEMI7emMpWhsq5uy5UKzW+kIA44yNAYz0VI6xySkiDk4gMTXXBG0PYEzl8rx8Is4vXr923vNuaqsnXO6noyvG3mtbbF1TKSvVvcRgIs3wePbyBp4ABw4UPt59t7OLorgmnrOFgausXogQQgj7mBNBnMrAmC4hcbmR55/98ttdvb9YEtlnCOGS3tj4sspHACrK/TRVBzll4yjVoVTh2eLUZK2ZWmpDtk8hee3sCIn0FLu2XLl8BKA84OO2TQ10GOUmwh6lupc43neFCSRQCGBUVcFNzg8NK6YHxjEK3cAB/EAjIHWpQgixgpgTQZwOYMQ9MEpVeJvsM4Twhnxe0xtPzdtzoVhtDWFbS0hiicIzzZUMjOoQr/QM2XqPjq44SsGdmyMLnrurvZH9nT/n7PA46+udadQtSsMVJ5BAIYCxaxcEnO9zVswdf3HG51NAv9Z6yqb1CCGEsIGZgeFUx/ZgwE9NKDB9X7c8+uTrQOm+e7JCyD5DCA/oG5skPZVn4zIzMKDQB+NlG/symP2VIg73wIDCJJL+sUnyeT13Y0MLdHQPct26WuqLCNCYAaeOrhgfvrXNlvWUulLdSxzvS9BYHby8GW8uB3fcAbff7sq6iumBccqJhQghhLCP0yUk5r3cLiE5OZhy9f5iYbLPEMIbrJhAYmprCPOdo+dIT+UIBvzLvt5sZlah0008AVpqQkzlNfFUhkYbSliS6SmOnh7h4buKq6Rrb6qiuSbIwW4JYNilVPcSnf1jczfw9Pvhi190fkGGYqaQCCGEWOEGk2nK/T5qQs6l+hUCGFJCIoQQK0FPvPBLmiUZGA1htIazwxPLvtZcnM4qnKm5phC0sKuR58sn4kzl9YL9L0xKKXZuifJid4x8Xi/8AiGKMJXL81Z/km3NcwQwBgdBu/dvTQIYQghRAuLJDJGqcpSyJ911LtHqcglgCCHECtEbS1Ee8LGmJrTsa20wR6na1MgznkwTKvMRLrc+u2Mhzcb/PnYFMDq6Y4TKfNy8sb7o1+xujzI8nuWN82O2rEmUnt74OJmp/NwNPO+7Dz7wAecXZbAtgKGU+rJSakAp9bMZxxqUUs8opbqMj/XGcaWU+pxSqlsp9bpS6qYZr3nIOL9LKfXQjOM3K6WOGa/5nDJ25Ve6hxBClLJYMu1o+QhApNL9EhIhhBDF6YmNs6EhbElfh9YGI4BhUyPPeDJDtCroaFDe1FJbCGDYNYmkozvGrZsiiyq92WlkaxzsHrRlTaL0mA08r14zq4FnPA7HjrkyfcRkZwbG3wIPzDr2SeDHWut24MfG1wDvAtqNPw8Dn4dCMAL4FHAbcCvwqRkBic8b55qve2CBewghRMkqBDCcTbWNVgUZnciSmco7et+ZrllbwzVr5+ieLYQQ4hK98ZQl5SNQ6E1RUea3LYAxmEy70sATMAIn0D9mfYbhhdEJugeS7Nqy8PSRmZqqQ2xvqaajS8ap2qEU9xKdfWP4FGxpqrr0G88/X/h4993OL8pgWwBDa/08MHvG0PuArxqffxV4cMbxv9MFLwN1Sqk1wF7gGa31kNZ6GHgGeMD4Xo3W+iWttQb+bta15rqHEEKUrEIJibObvWh1IWAylHIvC+NT77mWT73nWtfuL4QQK0EurzkdH7ekgScU+jK0NYQ5ZVsJSYZGF/pfAJT5fUSrgvSPWp+BYQYgdm1pXPRrd22Jcrh3mIlMzupllbxS3Esc70uwMVpJqGxWJtCBA1BRAbfc4s7CcL4HRrPW+gKA8bHJOL4OODPjvLPGsfmOn53j+Hz3EEKIkqS1nk63dVKksnA/6YMhhBDedmF0gkwuz8aINQEMKJSRnLEpAyOWTE8/Y9zQUhOypYTkhe4Y0aryuSc/LGBXe5RMLs+h3tnvHwuxeJ39ibn/HR44AHfeCeXuBBDBO0085ypg00s4vribKvWwUuqwUurw4KDUjAkhVqexiSkyubzjJSSNRgaGmwGM3/3GUX73G0ddu78QQqwEvbFCoGFjNGzZNTdEwpweGkdbPK0gn9cMpTKuTCAxNdcELW/iqbWmozvOzi3RJfUhuW1ThHK/j44u+Z3GaqW2lxjPTHF6aJxtzXOUzXz60/CHf+j8omZwOoDRb5R/YHwcMI6fBVpnnLceOL/A8fVzHJ/vHpfRWj+utd6htd7R2Lj4VC0hhFgJYqlCAMG9DAz3SkgujE5ywYY0XyGEWE3MEapWlZBAYZTqRDbHoMVB7LHJLFN57fgzbabmmpDlAYzjfQliyfR0Q87Fqij3c/OGeg5KHwzLldpe4q3+JFoz9wSS97wH7r/f+UXN4HQA42nAnCTyEPDdGcc/ZkwjuR0YNco/9gH3K6Xqjead9wP7jO8llFK3G9NHPjbrWnPdQwghSlIs4U4AI1pduF9cSkiEEMLTemMpQmU+mquXP0LV1GZMIrG6jMTM6nM3AyPE8HiWyax1/SbM/he725cWwADYvTXK8b4Egwl57oql6+wrjOO9rITk2Wfh0CEXVnQpO8eo/iPwErBNKXVWKfVx4LPAO5VSXcA7ja8BfgCcBLqBLwD/HkBrPQR8BviJ8efTxjGA3wa+aLzmBPBD4/iV7iGEECXJzIAwm2o6pbLcT6jMJz0whBDC43pjKTZGKi0ZoWpqixQCGFY38jSfaY0uZmC01BQCPVYGCjq6Y2xurGRNbcWSr7HbaP75QrdkYYilO96XoKLMPx2EnPbII66XjwAE7Lqw1vpXr/Ct++Y4VwOfuMJ1vgx8eY7jh4G3zXE8Ptc9hBCiVMWNEhKnG54ppYhUBl0tIRFCCLGwnniKrU2Lbxw5n3V1FSiF5aNUL2ZguFhCUlsIYPSNTdI6+5e8JUhP5XilJ86Hb2lb1nWuXVtDfbiMg10xHrxx3cIvEGIOnX0JtjZXXRrQHBmBV1+F//Jf3FuYwbYAhli5njp6jsf2dXJ+ZIK1dRU8sneb/BAUYgWLJdL4FDRUOp9uG60OupqBcdOGetfuLYQQK8FULs+ZoXHuv6bF0uuGyvy01IQsD2DEjaC42008Afos6otw5NQwk9n8kvtfmHw+xZ1bonR0D6K1plBpL5ar1PYSnX0J7rt61iDPjg7I5+Huu91Z1AwSwBCXeOroOR598hgTRk3fuZEJHn3yGIAEMYRYoQaTGRoqy/FbmBpcrMaqcs6NuNf46o8e2O7avYUQYiU4PzJJNqfZZOEEElNbQ5jTFpeQxJOFoHx92L0AhllCYlUjzxe6Y/h9ituvalj2tXZvifLPr1+geyBJe7O1WTWlqpT2EoOJNPFUhm0tsyaQHDhQGJ16++3uLGwGr4xRFR7x2L7O6eCFaSKb47F9nS6tSAixXPFk2vHyEVOhhER6YAghhFeZE0g2RqybQGJqawhbnoHhZlDeVFtRRjDgsyyA0dEV48bWOqpDZcu+1i6jCahMIxFL0dmXAOZo4Pnii3DrrVCx9B4tVpEAhrjE+ZGJRR0XQnhfLJl2vIGnKVpdzlAqQz6vXbn/v/vaEf7d1464cm8hhFgJemPWj1A1bYiEGUikmchYN60jnky7OkIVCj2emmtC9I0tP0A/Mp7h9XOj04GH5VpfH2ZTtJIOaeRpmVLaSxw3JpBcNkL1X/8VvvY1F1Z0OQlgiEusrZs7qnal40II74slM65t9qJVQXJ5zchE1pX7D49nGB6XJqJCCHElPbEUleV+Gqutf06YDS7PDFuXhRFLpl3tf2FqqQlZkoHx4ok4WsOuZfa/mGnXligvn4yTmcpbds1SVkp7ic6+BNGq8sv3jRUVsHGjK2uaTQIY4hJ/8M6tlx2rKPPzyN5tLqxGCGEFV0tIjAeglJEIIYQ39cZTbIhU2tLw0RzDaGUfjHgq49ozbabmWmsCGB3dMaqCAa5vrbNgVQW72qOMZ3L89PSwZdcUpaGzP3F59sXjj8OnPuXOguYgAQxxiQ1GA6eKMj8Akcpy/uyXr5MGnkKsUBOZHKlMzr0SEuNdMglgCCGEN/XGUmy0oYEnwAajr4aVfTDiLmYVztRcHaRvdBKtl1ci2dEV4/arIpT5rfu17I7NEfw+RYf0wRCLkMtr3upPsK15VgPPr3wFnnnGnUXNQQIY4hLPHh/A71M898g9BAM+3nvDWgleCLGCmYEDtzZ7jdMZGKWReimEECtJNpfnzPCELQ08AerDZVQFA5YFMCazOZLpKW+UkNSGSE/lGZuYWvI1TsfHOT00zm6L+l+YakJlXL++loPSB0MswumhcSaz+UsbeKZScPgw3HOPa+uaTQIY4hLPHh/k5rZ6mmtC3LE5wrPHB5YdWRZCuOdiAMOdzd50CUnCnQyMnVui7LSwrlgIIVaTs8MT5PKajTY08IRCs8tWCyeRmM+0Ri9kYBijVPuWUUZysHsQwJbn1K72Ro6dHWF03J0eVKtJqewlOudq4PniizA1BXff7dKqLicBDDHtwugEb14Y497tTQDs2d7Eqfg4J43u1EKIlcfMfHArA6Ouogy/TxFPuRPA+J372vmd+9pdubewl1KqVSm1Xyn1plLqDaXUfzKONyilnlFKdRkf643jSin1OaVUt1LqdaXUTTOu9ZBxfpdS6qEZx29WSh0zXvM5ZUeTACFcZOcEEtOGhjCn4tbsJc1nmhcyMKwIYLzQHWNNbYjNjdb/77+7PUpew4snJAtjuUplL3G8L4FSsLV5RgDjuefA74c773RtXbNJAENMe66zEAXeYwQw7t1W+Lj/+IBraxJCLI/bJSQ+nyJSWU4sISUkwnJTwB9ora8Gbgc+oZS6Bvgk8GOtdTvwY+NrgHcB7cafh4HPQyHgAXwKuA24FfiUGfQwznl4xusecODvJYRjeowAhl0lJABtkTBnhicsGacdN55pEQ9kYLQYAYylNvLM5TUvdMfZtSVqSwPVG1rrqAoGpIxEFO34hQQbGsJUlPsvHszn4R3vgOrqK7/QYRLAENOePT7AuroKtjZXAYXRV+1NVTwrAQwhVixzs9dQ6d67VZGqoGtNPB/68iEe+vIhV+4t7KW1vqC1/qnxeQJ4E1gHvA/4qnHaV4EHjc/fB/ydLngZqFNKrQH2As9orYe01sPAM8ADxvdqtNYv6UIt5d/NuJYQq8KpeIqqYMDWMsO2hjCZqTwDFpQSxqezCt3PwGiqKQRR+keXFsD42blRRiey7LK4/4WpzO/j9qsapJGnBUplLzHnBJI/+zP40Y/cWdAVSABDAJCeyvFCd4x7tzdeEgXec3UTh3qGSExK/ZwQK1EsmaE6FCBU5l/4ZJtEq8qJpdzJwJjM5pjM5ly5t3COUmojcCPwCtCstb4AhSAH0GSctg44M+NlZ41j8x0/O8dxIVaNnvg4G6NhWzIATOYoVSvKSAZdziqcKVTmpy5cRn9iaQGMDiMzws7eCru2RDk9NG7pGNtSVAp7iYlMjt54im0tMyaQ5PPuLWgeEsAQALxycojxTG66bMS0Z1sTU3kt0VshVqjBZNr1ZmeNVUHXmniK1U8pVQX8E/C7Wuux+U6d45hewvHZ939YKXVYKXV4cHCwmCUL4Rm9sZSt5SNwMYBhRSPPeDJDVdDdoPxMLTUh+kaX9nzr6Ipx9ZoaW4Mxu9obgYvNQoW4kq6BBFpz6QSST38arr++0MTTQySAIQDY3zlAMODjzs2XRoFv3lBPTSggZSRCrFDxZNr1ZmeRqnLiqbRMNBKWU0qVUQhe/IPW+knjcL9R/oHx0XyAnQVaZ7x8PXB+gePr5zh+Ca3141rrHVrrHY2Njcv/SwnhkMxUnrPD47Y28ARYV1+BT8EZCwIYMQ8802ZqrgktqQfGRCbHkVPDlo9PnW1zYyVrakPyRqRY0PG+BDBrAsn+/VBeDoGAS6uamwQwBFBo1HnH5silTVuAgN/HXVsb2d85aEnzJSGEs2LJjOupttGqIJPZPKnM6k6/FM4yJoJ8CXhTa/0/Z3zracCcJPIQ8N0Zxz9mTCO5HRg1Skz2AfcrpeqN5p33A/uM7yWUUrcb9/rYjGsJseKdGR4nr+1t4AmFXgxr6yo4ZUUGRipNxMWeTrM11wSXFMB4pSdOJpe3fTSnUopdW6K8eCJObhXv4586eo6dn32WTZ/8Z3Z+9lmeOnrO7SWtOJ19CYIB38WfB5OT8MornhqfapIAhuDkYJLe+Pj09JHZ9mxvIpZM87Pzow6vTAixXLFk2vUAhtkt3o0ykvuubuK+q+f+2SZWvJ3AR4E9SqlXjT/vBj4LvFMp1QW80/ga4AfASaAb+ALw7wG01kPAZ4CfGH8+bRwD+G3gi8ZrTgA/dOIvJoQTzBGqG23OwIBCGYlVJSRuP9NmaqkJEUummcotrldAR1eMcr+PWzc22LSyi3a1RxmdyHLs3Orcxz919ByPPnmMcyMTaODcyASPPnnM0iBGKewlOvsStDdX4fcZ1ZOvvALpNNxzj6vrmou38kGEK8zykNn9L0x3b21EqcJ5b19f5+TShBDLkM3lGRnPup5ua3aLj6fSjmyUZ3r4rs2O3k84R2vdwdx9KgDum+N8DXziCtf6MvDlOY4fBt62jGUK4VnmCFW7S0gANkTC/Msb/cu+TiyZ5sa2+oVPdEhzbYi8LvSbWlNbUfTrOrpj7NhYf1nmsx3MLI+OrkFuaF19+/jH9nUyMavB5kQ2x2P7OnnwRmv6LpfCXuJ4X4J7ts0ogzxwAJSCXbvcW9QVSAaGYH/nAFuaqmg1mizNFqkKckNrnfTBEGKFGUqZ4+bcLyEBGEy4M4lECCHE5XrjKWpCAerDZbbfq7UhTDyVIZleejPAXF4zlMrQ6KUeGNUhAPrHis8wHEykOd6XsG186mzRqiDXrKnh4Crtg3F+ZGJRx8Xl4sk0sWT60gaeO3bAJz8Jdd4LekkAo8Ql01Mc6hm6YvmIac+2Jl4/O8rAEkdFCSGcN5jwxrg58/6xpPMlJB/6m5f40N+85Ph9hRDC63pjhQaedo5QNW1oKGR5LKeR58h4hry+WJboBS21hQBG32jx++MXjPGpu2zufzHT7vYoPz09TGoZASSvWmP8fzDb2rriM2IWstr3Ep1zNfB897vhT//UpRXNTwIYJa6jK0Y2p69YPmK61whwPNcpY5iEWCni0xkY7k8hgULtshBCCG/oiaUcK+szR6meii89gBEzniFul0XO1FxjZmAUH8A42BWjLlzGtWtr7VrWZXa1R8nmNId6hhY+eYXZOUcmS0WZn0f2bnNhNSvTZRNILlyAEyfAo9PjJIBR4vYfH6A6GGDHxvnrCa9dW0NzTZD9UkYixIoR80gGRpnfR124zJUMDCGEEJebzOY4Pzph+wQSkxnAWE4GRjzpjWfaTJHKcgI+VXQAQ2vNC90xdm6OXmyW6IBbNjZQHvCtujKSXF5zpHeY9XUVrKu7mInxhw9ss6z/RSno7EvQUFlOo/nf1pe+BO3tMDzs7sKuQAIYJUxrzf7OAXZvjVLmn/+fglKKPdubONgVIzO1uE7LQgh3mAGDaLX7m71IZbkEMIQQwiPODI2jNWyMzt3/zGq14TJqK8qWNYlkcDqA4Z0MDJ9P0VQdpK/IAMaJwSR9Y5OO9b8whcr83LqxgY7u1ZVJ/czP+zkZS/FH79rOC5+8j44/uhelYDglGZ+Lcbw/wbbm6ovlZM89B9ddBw32T8lZCglglLA3zo8xkEgvWD5iundbE8n0FId7V1/6mRfIDGthtXgqQ6jMR6UDXc4XEq0KSgmJEEJ4hDmBxKkMDChkYZxaVgaGNxpTz9ZcGyo6A8PMgHCy/4VpV3uUt/qTiyp38brHnz/B+voK3vW2FgDW14e5e2sj3/jJGbKLHG1bqvJ5TVd/4mL5SCYDL74Id9/t7sLmIQGMEmaWg9xTZABj55Yo5X6fTCOxgRMzrEXpiSXSRCqDjjRoW0i0OuhKBsYvvn0Nv/j2NY7fVwghvKw37twIVVNbQ3h5JSSpNAGfoiZk/9SUxWiuDhU9heSF7hgbIuErTv6z067pcaqro4zkcO8QPz09wv+1axOBGZnkv3bbBgYSaX78pnW/r6zmvcSZ4XHGM7mLE0gOH4aJCQlgCG96tnOA69fX0lhkenllMMBtVzXwbKcEMKw23wxrIZZqMJn2RPkIQLSyfDr910kfvWMjH71jo+P3FUIIL+uJjVMXLqMu7Fw5RlskzNnhcXL5pTUGjCUyNFSW43Owd0QxWmpD9BcxhSSby/PyySFXsi8ArllTQ6SynI7u1RHA+JvnT1IXLuODt7RecvzebY201IT4+qHTlt1rNe8lLmvgeeBA4eNdd7m0ooVJAKNExZNpXj0zMj1dpFh7tjdxcjDFKSNyL6whM6yFHeLJDI0eqRWOVgVJTE6RnsotfLKFJjI5JjLO3lMIIbyuN5ZytHwEChkY2ZzmwujS9jbxVNpz5SNQmESSSE8tOKL01TMjJNNT7Ha4/4XJ51PcuSVKR3cM7dHpEsU6MZjkX9/s56O3byBcHrjkewG/jw/d0srBrkFOL2PqzUyreS9hjlDd2mwEMD7+cfj+96Gx0cVVzU8CGCXqwFuDaE3R/S9Me4yAh5SRWOtKs6qtnGEtSk8sWSgh8QIzE8TpPhi/8ZVD/MZXDjl6TyGE8LreeMrR8hG4OIlkqY08B5MZT41QNTXXFJ5vC/WWONgVw6fgjqvcCWAA7G6PMphI09mfcG0NVvjiwZOU+X187ApZER++tRUF/ONPrMnCWM17ic6+BG0NYSqDRiCoqQl+4RfcXdQCJIBRovZ3DhKtCnLdusXNoN4QqeSqxkoJYFjskb3bmJ0RGSrzyQxrsWT5vCaeyhCt9sZmL1JZWIdMIhFCCHdNZnNcGJ10JQMDWPK74vFk+uKYRw9pqSmM71xoEskL3TGuW19Hbdi9Hh5m9sdK7oMxmEjzTz89x/tvXn/FMvg1tRXs2d7Mtw+fkemJCzjeN3axfOT4cfif/xOGvD2wQQIYJWgql+dA5wD3bGtcUh3hnm1NvHJyaMFUOVG8m9rqyWuoCQUw/x/5lZvXyQxrsWSjE1lyee2ZdFu3MjCEEEJc6pQRQHBqhKppTW2IgE8tOQMj7tUMjNpCAGO+DIyxySyvnhlht0v9L0xraivY3Fg5PQ1lJfrqi71kc3l+a/dV8573a7e1EUtmeObn/Q6tbOWZzObojY9fbOD59NPwB38A2ay7C1uABDBK0E9PjzA2OTVdDrJYe7Y3kcnlV00TIC944qdnUQp+9Lt3cfLP3s2GSJiTA9bU7YnSZGY6RDwSwDDfNXOjkacQQoiLzBGqTpeQBPw+1hNzQH8AACAASURBVNdXLCmAkUpPMZHNeeaZNlNzjRnAuPLz7eUTcXJ5zS6X+l/MtLu9kVd64o73pLJCKj3F114+xf3XNC/47/eurY2sq6vgH1455dDqVp7ugSS5vL60gef27dDc7O7CFiABjBL07PEBAj615B+iOzY2UBUMTI9hFcuTz2v+6chZdm2JsrauAqUUH7h5PS+djFvWfEiUHjNQEPXIu1Xmu2aSgSGEEO4yR6hudDiAAdDaEF5SAMN8dnglq3CmqmCAqmCAvnkmkXR0x6go83NjW52DK5vbri1RJrN5jpwadnspi/atw2cYncjy8F2bFzzX71P86q2tvHgiPh20E5cyJ5Bsb6mGqSk4eNDT41NNEsAoQfuPD7BjY/2S52iXB3zsbo+yv3NgxXcx9oKXT8Y5NzLB+29eP33sV25ej0/Bt4+ccXFlYiUzN3teqRcOlwcIl/sd74Hx/pvXX/LflhBClLreWIpIZfmS94HL0bbEAMbgdFahN4LyszXXBOctIenojnHbVQ0EA34HVzW32zdHCPjUiuuDMZXL88WDPezYUM/NG+qLes0Hd7QS8Cn+cZkjVVfrXqKzb4zygK/QD+fVVyGRkACG8J5zIxN09ieWXD5i2rO9if6xNG+cH7NoZaXr20fOUh0KsPfalulja2oruGtrI08cObvkeemitHmthAQK75w5HcD4wI5WPrCjdeEThRCiRPTEUq5kXwBsiIQZGc8yOrG4Gvu4mVXokclaszXXhK4YwDg/MsHJwRS7XO5/YaoKBrixrW7FlYL/87ELnBuZ4OG75u99MVNTTYh3XlNo5jmZXXrJzGrdSxzvS7ClsYqA3wdvvgl+vwQwhPeYZR/LDWDcY4xflTKS5UlMZvnhzy7wnuvXEiq7NCr/wR2tXBid5GDXoEurEytZLJnG71PUVbjX7Xy2SFW54yUkQ6kMQykpWxFCCFNvPOX4BBKTOYnkzCKzMOLGz3GvTNaaraUmdMUeGGamw+72RieXNK9dWxo5dm6U4RXyfNRa8/jzJ7mqsZJ3XL24/gwfua2N4fEs+97oW/L9V+teorMvcbGB50c/CqOjsHatu4sqggQwSsz+4wO0NlSwubFqWddprA5y/fpanu2UAMZy/ODYBSaz+TnT0t5xdTMNleV8+/BZF1YmVrpYIkOksnxJk4bs4kYGxm///RF++++POHpPIYTwqvHMFP1jaTY5PIHE1GqOUl1kACOWKDw7Giq9GcBoMjIw8nNkzR7sjtFYHWRr8/L23lba1R5Fa3jhxMrIwnjxRJw3zo/xW7uvWvS+ZufmKG0NYf7hlaWXkazGvcRwKsNAIn2xgSdApTuBzcWSAEYJmczmeOFEjHu3NaHU8n+puXd7E6+eGZlO6xOL9+3DZ9ncWMmNrZc3dSoP+HjwhnX8y8/7VmXUV9grnkp7qnwECg1FnQ5gCCGEuKg3Zo5QdTcD49Qim5THUxlqQgFP9JCYS0tNkKm8Zmj80v1aPq95sTvGri1RS/beVrl+fS3VocCK6YPxN8+fJFoV5JduXLfo1/p8io/c1sahniG6+hM2rG5lMht4bmuphtdeg3vvhWPHXF5VcSSAUUJePhlnMpvn3mWWj5j2bG9CazjwlpQ4LEVPLMXhU8O8/+bWKz7UPnRLK9mc5qmj5xxenVjpBpMZz0wgMUWrggylMtLXRQghXDI9gcSlEpLqUBkNleWLzsAYTKY9OYHE1FJbGKU6exLJm31jxFMZz/S/MAX8Pu64KsLBrpjnG/K/eWGM598a5Dd3brys3LpY7795PWV+xdeX2cxzNensK/Qx3N5SA88+C889B/XFNUd1mwQwSsj+4wOEygo/sKzwtrW1RKuC/Fj6YCzJE0fO4FPwyzddOZq8raWa69fX8q3DZzz/gBHeEkukPTOBxBStCpLXMDwuGUVCCOEGc5ykWxkYUMjCWHQPjGTasxNIoFBCAlzWyNPMcNjpsQAGwO72KOdGJuhdZDaM077w/EnC5X5+/bYNS75GtCrI3mtb+KcjZ5fVzHM16exPUFtRRnNNEA4cgM2bYf3KmLQiAYwSobXm2c4Bdm6OLjl6OZvPp7h3WyPPvzVINpe35JqlIpfXPPnTc9y1tZFm46F3JR+8pZXjfQmOnRt1aHVipdNaGyUk3trsmeuRMhIhhHBHbyxFtCpIVTDg2hraGsKcGkot6jXxZMbbGRjTAYxLn28d3THam6qmMzS8ZJfRVLTDw83iz49M8PRr5/nQLa3UhpfXlPzXbtvA2OQU33/9gkWrW9mO9yXY1lKN0hoOHlwR00dMEsAoEScGk5wZmuAei8pHTHu2N5GYnOLIqWFLr7vavdAd48LoZFEzpQsTSnx86/AZB1YmVoNUJsdkNu+5zZ65Hicnkfz67Rv49duX/q6NEEKsJr3xlGsNPE1tDWHOj0wu6s2vmMczMBqrgygFfTMyMCazOQ71DLGr3XvZFwAbI2HW1VVw0MN9ML7yQg8a+PiuTcu+1u1XNXBVYyVff+XUol+72vYS+bzmLXMCyc9+BkNDEsAQ3vOsReNTZ9vVHqXMr2Sc6iI9ceQstRVlRY2CqgmV8e63reG7r56XtDdRFLNbu1cDGE5mYLzn+rW853rvjwQTQggn9MTGXet/YWqLhMnlNedHJoo6fyqXZ3g867ln2kxlfh+RyiD9M3pgHDk1THoqz26PBjCUUuxuj/LSiThTHsykHpvM8o+HzvAL161hff3yg25KKT5yaxs/PT3CmxfGFvXa1baXODcyQSqTKzTwzGTg/vvhnnvcXlbRJIBRIvYfH2RbczXr6iosvW51qIxbNjZMB0jEwkYnCrOo33v92qLLeT6wo5XE5BQ/+tnSZ1iL0hFPFQIEXnu3ymwqOphwLoBxfmSi6E2yEEKsZonJLLFk2tX+F3BxEkmxjTzNSWxem6w1W0ttkP7ExQDGwa4YAZ/i1k3W9J6zw672KIn0FK+d9V6Z8tdfOU0yPcXDd11l2TV/5ab1lAd8fH2RI1VX217CnECyvaUaduyAffugrc3lVRVPAhglYGwyy096hyybPjLbnu1NdA0kF92QqVR9//XzpKfyfGBH8Y1ybtvUQFtDmG/+RMpIxMIGE4XNntferaqtKKPMr4g7OBb49775Kr/3zVcdu58QQniVObp00woLYMSMssNopbeC8rO11IQumULS0T3ITW31rvYbWcjOzVGUwnPjVNNTOb7c0cPOLRHetq7WsuvWV5bzC9et4TtHz5FKTxX9utW2lzAnkGxtqoKREZdXs3gSwCgBHV0xpvLa8vIRk3nd/Z2ShVGMJ46cZWtzFdct4geyz6f44I71vHQyzmmPd4sW7jNLNBqrvRXAUEoRqQxOl7gIIYRwzvQEEpdLSFpqQpT7fUXvZ8yswqjHnmmzNdWEpqeQDKUyvHF+zLP9L0z1leW8bW0tHd3eauT53VfPM5BI82/v2mz5tT9yWxvJ9BTfe+285ddeKY73JVhfX0H1yS5oaIDvfMftJS2KKwEMpVSvUuqYUupVpdRh41iDUuoZpVSX8bHeOK6UUp9TSnUrpV5XSt004zoPGed3KaUemnH8ZuP63cZrlfN/S+949vgANaEAN7XV2XL9qxqr2BgJSxlJEboHEhw9PcL7b17PYv9Z/srN6/Ep+PYRycIQ8zObZDZ48N2qSFW5TCERQggX9E6PUHW3iafPp1jfULGIDAyjLNKDz7SZWmpCDI9nSU/lePFEDK3xfAADCms8enqE5CIyEuyUz2u+8PxJrl5TY0v/kB0b6tnaXMXXDy2ujGQ16TQbeD73HGgN11/v9pIWxc0MjHu11jdorXcYX38S+LHWuh34sfE1wLuAduPPw8DnoRDwAD4F3AbcCnzKDHoY5zw843UP2P/X8aZ8XvNc5yB3bW0k4Lfv/+57tzfx0ok4ExlpMjmfJ46cw+9TPHjjukW/dk1tBXdtbeSJI2fJ5bUNqxOrRSyZpi5cRpmN/80vVbQq6GgJiRBCiILe+DjNNUHC5e6XNGxoCBcdwDCD8l7PwDBHqQ6MpenoilEdCvB2C8sf7LJ7S5SpvOblE3G3lwLAc28N0DWQ5OG7Ni36zb5imM08Xz87yjEP9v6wW3oqx8lYqtDA88ABaG2FTcuf8uIkL+1u3wd81fj8q8CDM47/nS54GahTSq0B9gLPaK2HtNbDwDPAA8b3arTWL2mtNfB3M65Vcn52fpRYMm1b+Yhpz/Ym0lN5XjzhrRo6L8nlNd85epZ7tjbSVL20eeAf3NHKhdFJDnp4ZrdwXyyZ9lz/C1O0SkpIhBDCDb3xlOvlI6a2hjCn4+MUturziyUzlPt9VHu4lwRAU03huds3NsnBrhh3XBWx9c1Dq9y8sZ5QmY+Obm/s4f/mwEnW1ob4xbfbN/Xjl25aT6jMx9cPLX6k6kp3YiBFLq/Z1mwEMO6+G1ZYsYJb/1Vp4F+UUkeUUg8bx5q11hcAjI/mb9zrgJk582eNY/MdPzvH8csopR5WSh1WSh0eHFydvxA+e3wApeDurY223ufWTQ2Ey/1SRjKP57sG6R9LL6p552zvuLqZhspyvn347MIni5IVT2Y8m2obrSonlswUtWm1wm/tvorf2m1dB3MhhFipemMp1xt4mlobwiTSU4yMZxc8N5ZME6kqt+XdeCu11BbenDrUM8S5kQnPjk+dLRjwc+umiCfeHHvtzAiv9Azxb3ZtsjWLtLaijPe8fS3fffU8icmF/w2upr1EZ3+hgefbExdgYKAQwFhh3Apg7NRa30ShPOQTSqm75jl3rp9WegnHLz+o9eNa6x1a6x2Njfb+gu+W/ccHuKG1zvbRU8GAn11bojx7fMCxX0xWmieOnKU+XMae7c1LvkZ5wMeDN6zjX37eNz1WTIjZYsm0Z1Nto1VBMrk8CYdqbd9xTTPvuGbp/80J4aanjp5j52efZdMn/5mdn32Wp46ec3tJYoUam8wST2VcH6Fq2mBkghRTRhL3cFbhTGYJyT8dKbzJtKt95fxusXtLlBODKS6Mujsq9PHnT1IdCvDhW+0f6fmR29oYz+T47qsLN/NcTXuJ430JyvyKde2t8Fd/BXv3ur2kRXMlgKG1Pm98HAC+Q6GHRb9R/oHx0Xwr/yzQOuPl64HzCxxfP8fxkjOYSPPa2VHu3WZv+Yhpz/YmLoxOTs8WFheNjGd45o1+3nfDOsoDy/vP7kO3tJLNadnIiisaTKZp9OhmL1pdyAxxqozkxGCSE4NJR+4lhJWeOnqOR588xrmRCTRwbmSCR588Jj/7xZL0emQCickcpXqqiABGLJkhUuXNrMKZaivKKA/4OBlLsa6ugo0Rd5ulLobZbPSgi+NUT8fH+eHPLvBrt21wZPTsDa11XL2mhn945fSCb76upr1EZ1+CzY1VlLU0wyc+UeiBscI4HsBQSlUqparNz4H7gZ8BTwPmJJGHgO8anz8NfMyYRnI7MGqUmOwD7ldK1RvNO+8H9hnfSyilbjemj3xsxrVKyoG3Cqlgdve/MN1r3EfKSC73vdfOk8nlef/NSy8fMW1rqeb69bV86/AZyXYRl0lP5UhMTnm2hCRSWQisONXI8z8/eYz//OQxR+4lhJUe29fJRPbSxtgT2RyP7et0aUViJTNHqHqnhKQCgDOrKAPju6+en26yPjyeKeqdfa84fmEMn4I/fOJ117K9vthxEr9P8Zs7NzpyP6UUv3ZbG29eGOPVMyPznrua9hKdfQm2N1fBN78JfX1uL2dJ3MjAaAY6lFKvAYeAf9Za/wj4LPBOpVQX8E7ja4AfACeBbuALwP/P3p3HR1Vfj/9/newJ2QgEyA7IEkA2iYDiUrAKaqtUxa1Waxet1u61amu12m/V1l+rtfXTXa1bFVBR64ILWFxADTtC2JcshGyQfc/798e9g0NIQgKZufcm5/l4zCOZmzt3TiaTmTPnvt/nfTOAMaYC+DXwqX25194GcBPwT/s2O4E3gvB7uc7yvBKGxEUyITU+KPc3ND6KCanxLNcCxlEWrS4ge1hcr/0tFuRkkFdczcbC/tc9WXXN7d3afUmoNvJUqmtFhzoeyl10qJ6mlrYgR6O8bk+ZVSjIcsmogJiIMJLjItlbXtvlfsYYymrdPwLDN2LKV8Coa2r1zIipJWsL+flLm/AtcOfEaK+K2iYW5ubzlalpDI0/vkb3x+PiKanERITy7Mf9Y0nVyrpm9lc2ML31IFx5JbzszXP8QS9gGGN2GWMm25cJxpjf2NvLjTHnGGNG218r7O3GGPNdY8xJxpiJxphcv2M9ZowZZV8e99uea4w52b7NLaYfnqZubm1jxbZSZo8dEtSmR3Oyh7Bm30EOan+Gw7YWV7OhoJIFORm99re4aEoqkWEhLMzNP/bOql8pq7EKA249W3V4CkmNFjCU6kpqYnSH2w0w5d63+OYTn/LEh7vZVVqjo/HUMe0pryU1IYqo8FCnQzkssxtLqVY3ttDU0sbgAe58T/Px8ogpN8T+1Mq9NDS3ccNZwW2UGRcVzsVTUnl1QxGV9cdu5ul1ecVWA89Tdq2zNniwgSe4axlV1Yty9xykurGF2dnBbSA0J3sIbcZacUNZXlhTQFiIMH9K7y0HFR8VzgUTU3h5XREN7d50VP/mG4Hh1rNVSTERiFhzmpVSnbt17lii233YjAoP4ZtnjODSU9LZWVrDr17dzJzf/48zfrucO17cwBsb91PZjVUdVP+zu6zWNQ08fbKSYsiv6Lpp5OejCt35nubT1Ygpt3M69vqmVv69cg/nZA9h1JC4oNynv6unZ9HQ3MZLa/r+Cn9bD1h9CjM35cLQoTB2rMMRHR93L6isjtvyrSWEh0rQOyBPTk9k0IAIluWVcPGUDlev7VeaW9t4cU0hc7KH9PpKMJfnZPDS2kLe3FTM/Kn6WCtLqT2ywa1NPMNCQxgYE6EjMJQ6Bt/r+oNLt1J0qJ7UxGhunTv2iNf7feV1rNheyoptpfx3/X7+80k+IWI1pztzdDJnjUlmcnoCYQFcjlB5w57yWs4/OcXpMI6QkRTDS+sKaWxpJTKs45EhvveKQS4fgZGaGE1hBx/4OxtJ5SadxW6Au17exG3zshkQwKaai9cUUFHbFPTRFz4T0xOYlJ7As5/s47rTh7t+ud4TkVdcTVxkKNErP7BGX3j0d9UCRh+1PK+E6SOSgtLF119IiHD22GSW5ZXQ2mYIDfHmP0ZvWbGtlLKaxl5p3tnejBFJZCbF8Pyn+VrAUIcdTvZcOgIDYNCA4BUwvjdndFDuR6lAmD81rcvX98xBMVwzKItrZmbR3NrG+vxDrNhWyortZTyybDt/fHc78VFhzBo12C5oDCZ9oNUDYcnawi6LI6rvOFTXxKG6ZkYMdkf/C5/MpBiMgcKD9YxMju1wn3KXT4v0uXXuWO54ceMRUzGiw0O5da77z3B3FHtUeAgzhifx1Kq9LMsr4XeXTuL0UYN7/b5b2wz/fH8XkzMSmT4iqdeP311XT8/k9hc3krv3IKcOPzqOvpJLbC2u5syIWqSgwLPTR0ALGH1SfkUd20tquOJUZ5bFmZM9hBfXFLJ230FyOngR6E8W5RYwaEDE4RVaelNIiHB5Tjr/31vb2FdeR6ZLGnMpZ5XXNBETEUpMhHtf3gfHRh4eFhxovqXhlOrrwkNDyBmeRM7wJH583lgO1TXx4Y5yu6BRyhubrG7zIwcPIC0xio93H6Sp1WoG6mvaB2gRow/a7bIlVH18DUX3VdR1WsDwTTcc7OKiPHRvxJRbdRV77p4Kbl28gav/+THXzMzk9vPH9erJ0bc+K2ZveR23zct2dOTDlyen8pvXtvDsx/s6LGD0hVzCGMO24mrGTR0De/dCbMf/c17g3gxXHbflW61VQIK1fGp7Z45OJjREWJZX0q8LGBW1Tbybd4BrTxtOeICG7146LZ0/vL2NRavz+cl57q/yq8Ar88Byc4PjItlY0PWSZb3lsyJrpZ4JqQlBuT+l3CIxJoILJ6Vw4aQUjDHsLK1hxbYyVmwv5b2tR/ep8jXt88IHLtUze8rdtYSqT2bS5wWMzvhG6w106dLg/o41YsrNOos9Z3gSr3//TH7/1lb+9eFulueV8rvLJjGrF0ZjGGP424pdZA2KYe6EYSd8vBMxIDKM+VPTeD43n7u+NP6o51tfyCUKD9VT3djC2GHxkJnpdDgnRCdF9kHL8krIGhTj2BtVQnQ4OVkDWdbPl1N9eV0hza0mINNHfFISojlrTDKLVxccXrpL9W9WAcPdiZ41hSQ4IzDufXUz9766OSj3pZRbiQijhsTxjTNG8MT10+nsPKcXGg6qnttdVoeI1XPCTZLjIokKD2FfeecFjPKaJgbGhAfsRJA6tuiIUO780ngWf+c0IsNC+Oo/P+bnL22kuuHEGgZ/uucg6/IP8a0zRrhiyvnVMzJpamnjhQ6aefaFXGJrsdXAc+6ffwVLlzobzAnSV4M+pr6plZU7y4O+fGp7c7KHkFdc3WFToP5i8eoCTk6LZ1xKfEDv5/KcDPZXNvC+rvyisJK93m4Y29uS4yKpaWzRFXSUckhnjQWT49z92qGOz56yWlITol21hCpYhbXMpBj2HmMEhtvf0/qLaVlJvP6DM7nxrJE898k+5j60ghXbjj/3/PuKnSQNiOCyac5MeW9vXEo8p2Qm8uwn+/rk0tR5xdWkVx5gyDNPwPbtTodzQrSA0ces3FVGY0ubY9NHfHz3v7yfjsLYXFTFZ0VVXHZK4EZf+JwzbggDY8JZlNv3l39Sx+aJKST2CBFdiUQpZ3S0RCtAZX0Tn+yucCAiFUh7ymtdN33EJzMphvwuChjlNU2uH1XYn0SFh3LHBeNYfNPpREeEcu1jn3D7Cxuo6uFojB0l1byzpYRrT8siOsI9hbWrZ2Sxq7SWVbv63uvg1uJq5pZtta54uIEnaAGjz1mWV0J0eCgzRjrbe2LUkFjSB0b32wLG4tUFhIcKFwVhKdnIsFC+MjWdtzYXU1EbnGH5yp1a2wwVtU0kuzzZ8y2HF6xpJEqpI82fmsb9l0wkLTEaAdISo7n7y+NJGxjD1/71Me9uOeB0iMe0ZG0hsx5YxojbX2PWA8tYsrbQ6ZBcyRjD7rJahrtsBRKfjKQY9lXUdXrGu6xWR2C40SmZA3nt+2fynbNPYmFuPnMfWsF7W7uf8/9jxW6iwkO49rThgQvyOHxpUgrxUWE88/Fep0PpdVuLq/lC8WZISoIJE5wO54RoAaMPMcawPK+UWaMGd7qedrCICHOyh/DhzrJ+N0y8qaWNJesK+eK4oSQFqenU5aem09xqNIHr5w7WNdFmcH2yN9gepl6uIzCUcsz8qWl8ePscdj9wIR/ePofrZ41g0Y2nMXZYHDc8tZqX1rp3VN+StYXc8eJGCg/VY/h8FRV9DzzawbpmqhtaXLcCiU9WUgx1Ta2dFrTLqhsZ7IEGnv1RVHgot5+fzYs3zyI2MoyvP/4pP1u8nsr6rkdjlFQ18NLaQhZMywhantxdUeGhXDotnaWfFfepUaJNLW3sLK3h5B3rrdEXId4uAXg7enWEbQdqKDxU7/j0EZ852UNoaG5j5a5yp0MJquVbS6iobQpo8872sofFMzk9gYW5+X1y3p7qHt+brU4h+dzP5o3lZ/N0hR6lumNQbCTPfnsmM0Yk8aPn1/P4h7udDqlDDy7No77dyRHfKirqSL4lVF07hWRQ5yuRNLW0UdXQ4vr3tP5uSkYir37vDG7+wkksXl3A3IdWdDkC+/GP9tDS1sa3zhwRxCi776szMmluNSxe/XkR1+u5xK6yGiIa6pCEeJg92+lwTpgWMPoQ3/Kps7OTHY7EMnPkIKLDQ/vdNJLFqwsYHBvJ2WOC+3dYkJNBXnE1Gwsrg3q/yj3Kqq0zWG6fL+xLRoMxhWRaVhLTsvrvcs5K9VRsZBiPff1U5k4Yyj2vbuYPb211VWF8f2U9hYcaOvyZrqJytD12AWO4WwsYSVZcHfXBKK+1itxuH1WorJELP5uXzUs3zyI+Oozrn/iUny5aT2XdkaMxahpbeHrVXuadPIwsl44KGjUkjunDk3j243202Sv8eT2X2FpcTV1ENPuXfwS33OJ0OCdMCxh9yLK8EsalxJOS0HF38WCLCg9l1qhBLMsrcVXyE0hlNY0szyvhklPSCAvykl8XTUklMiyEhbn5Qb1f5R6+ZG+wy1cSiAoPJTYyLCgjMFbvrWD13r7XjEupQIoKD+XRq0/hipwMHlm2g1++vMnxpbqNMby4poDzHlrR6TKwna2u0p/tKa8lRCBjoDt7YKQPtP5meztYSrW8xhtFefW5yfZojFtmj+KltYWc9/D/WJZ34HDPmpPvXkp1Qwtjh8U5HWqXvjozk30VdXy4swzwfi6RV1xNmMBJybHg4CqVvUULGH1EZV0zq/ceZI5LRl/4zM4eQsHBenaU1DgdSlAsWVtIS5sJ6vQRn/iocC6YmMLL64r6Xd8RZSmttgsYA9xdwAArIQ3GCIzfvbmV372pw8qV6qmw0BAeuHQiN549kqdX7eMHz62lqaXNkVjKahr5ztOr+fHC9YwdGsfPLxh31CoqYSHCrXO9O8Q7UHaX1ZI+MIaIMHem/FHhoQyLj+pwComvyK0jMLwlMiyUn84dy5KbZzEwJoJvPJHLTxaup9BvhNRf39vl6p41804exsCYcJ79eB/g7VxiydpCHvtgN0se/wFPzbve1Y97d7nz1Uz12IrtpbS2GWaPdUf/Cx9fPMv6wTQSY6z5cpPTExgz1JnK8uU5GVQ3tPDmpmJH7l85q6ymiYjQEOKjw5wO5ZgGxUZqE0+lXE5EuOP8cdx+fjb/3bCfbz2ZS11TS1BjeHPTfs57aAXL80r5+QXZPH/jaXz7rJFHrKISHR5Ka5sha5A7Rxk4aU95rWunj/hkDophX0XtUdvLdASGp01MT+CVW84gLjKM1nYjsd3esyYyLJTLpqXz1uYDoDpnNgAAIABJREFUlFR1PGXNC3wNjxMOlnLygZ0UtYX3iYbHWsDoI5ZvLSExJpypmQOdDuUIqYnRZA+L491+UMD4rKiKvOJqR0Zf+MwYkURmUgzPf6rTSPqj8ppGBsVGIB4YHmiNwNAChjp+IvKYiJSIyCa/bUki8raIbLe/DrS3i4g8IiI7RGSDiJzid5vr7P23i8h1ftunichG+zaPiBf+sQLkO2efxG8vncgH20u55p8fc6gu8KOnKuua+eFza/nO02tITYziv98/gxvOOonQEOvP4L+Kyse/OIfUxGh+9Pw6ahuDW2BxM2MMe8rqGOHywk6mvZRqe+UeaUytOhcRFkJNJ/+Tbu9Zc9X0TFrbjKenZj+4dCv1za3MzLfeJj/OnOj64lF3aAGjD2hrM/xvaylnj0k+/MbuJnOyh7B678GjGvn0NYtXFxARGsJFk9MciyEkRFgwLZ2Vu8rZ18F8UtW3ldkFDC8YHBsZlCkkqk97ApjXbtvtwLvGmNHAu/Z1gPOB0fblBuAvYBU8gLuBGcB04G5f0cPe5wa/27W/r37lilMz+b+vnsKmwiqu+NsqDgTwrOTyrSWc9/D/+O+G/fzwi6N56eZZXY5sjI8K5w+XT2ZvRR3/77XNAYvLa8pqmqhpbHH/CIykGA5UNR41/bWsppGo8BBiIkI7uaXygs5607i9Z83I5FhOP2kQ//nEmwWMxpbWw9N2vrDzU1okhJKYRMD9xaNj0QJGH7C+4BDltU2uWT61vTnZQ2htM6zYXup0KAHT2NLKknWFnDthKAkx4Y7GcllOOiKweLU3X3DV8SurafLMmapBsZEcrGuipdWZOfXK+4wxK4D2XdUuBv5tf/9vYL7f9ieNZRWQKCIpwFzgbWNMhTHmIPA2MM/+WbwxZqWxulA/6XesfmveySk8cf2pFBys49K/fHR4hYveUtPYwh0vbuD6xz8lITqcl26exQ+/OIbwbjTFnjFyEN85+yT+80k+b28+0KtxedWecnsFEpeu9uDjm/rTfiWScvs9rR8PfuoTbp079qieNdHhoZ7oWXP1jEwKD9VzyGMnYfMr6ljw15WHr5+z81NCTRu3rFoIuL94dCxawOgDlueVECJw1mh3NfD0mZo5kMSY8D69nOqyLSUcqmt2dPqIT0pCNGeNTmbR6gLHu8ar4CqvafRMASM5NgJjoCLAQ9Hv+vJ47vry+IDeh3KVocaY/QD2V19lPw3wr+oW2Nu62l7QwfZ+7/RRg/nPDTOpa2rlsr+u5LOi3lm6e+XOcuY9vILnPs3nxrNH8sotZzAxPaFHx/jRF8cwITWe217YQEm1d+et95bdLl9C1ScjySpgtJ9GUlbbpA08+4D5U9OO6FmTlhjN/ZdMZP5U97+knjd+GINjI0iMCfdMLvHO5gNc+Mj77C6r5RuzhpNZf5ABzQ0IsGDjO2Q0VnqieNQVLWD0Acu2ljA1cyADB7hz6HhoiHD2mGTe21baZz9QL15dwND4SNcUka44NYP9lQ18sKPM6VBUkBhjKKtp8tQUEoCy6sAWMCakJjAhtWcfglSf1NEpXHMc248+sMgNIpIrIrmlpX13pKG/SemJLLzxNCJChSv/topPdh//8oINza3c8+pnXPWPVYSFCIu/cxp3nD+OqPCeTxuICAvh4SumUNvYwm2LN/SbJdw7s6esltAQObxUqVtldVbAqG5ksEtzW9Uz/j1rPrx9jieKF2C9pkzOSOTj3RV86ZEPmPXAMtc2wGxubeP+N7bwrSdzyUiK4bXvncldX57AE/vfos0exRRq2vh34VLPPP6d0QKGx5VUNbCpsMq100d85mQPoaK2ifUFh5wOpdeVVDfw3rZSvjI13TU9SM4ZN4SBMeEs1Gae/UZVQwtNrW0ke+Rsle+sWnltYBt5frC9jA+2ayGvHzlgT//A/uob+lcAZPjtlw4UHWN7egfbj2KM+bsxJscYk5Oc7I4idjCMGhLLoptOJzk+kq/962Pe3dLzaRtr9x3kgkfe5/EP93DdaVm8/oMzmZaVdEJxjbaXWV2+tZRn7CUQ+6s95bVkDIzu1hQcJyUNiGBARCh72/XuKq/1zqhC1TctWVt4OIcwQOGheleu4lFc2cDV/1jF3/63i6tnZPLCTaeTOSgG9u9n5KuLiGi1GqlGtLYw8r+LoNjbqxW6+xVNHdN7W62zPW5bPrW9s8ckEyL0yWkkS9YW0tpmXDF9xCcyLJSvTE3nrc3FVNRqo8T+wGvd2n3L4gV6JZI/LdvOn5ZtD+h9KFd5BfCtJHId8LLf9mvt1UhmApX2FJOlwHkiMtBu3nkesNT+WbWIzLRXH7nW71jKlpYYzaIbT2PssDhueGo1L64pOPaNsPpGPbg0j0v/8hENTa08860Z3HPxycRE9M4S0NeelsVZY5L5f69tZmdpTa8c04t2l9W5fvoIWMv1ZiTFHNEDwxhDuYdGFaq+6cGlW2lsObJXl9tW8fhgexkXPvI+nxVV8fAVU7jvKxM/H8H2619DW7teY62t1nYP0wKGxy3LK2FYfBTjUjrvzu0GiTERTMsayLI+VsAwxrAot4CpmYmMGhLrdDhHuPzUdJpbjeuqxCowfCt6eCXZGxwXnCkkqu8Skf8AK4GxIlIgIt8EHgDOFZHtwLn2dYDXgV3ADuAfwM0AxpgK4NfAp/blXnsbwE3AP+3b7ATeCMbv5TWDYiN59tszmTEiiR8vXM9jH+zucv/NRVVc/OcPeXT5Ti49JZ03f3QWs0YN7tWYRIQHL5tEdHgoP3p+Hc39sFmwMYa95bWub+DpkzUohr1+BYzK+mZa2oz2wFCO6my1Djes4tHaZvjjO9v52mMfkzQggldumXX01JCVK6GpXZ7V1AQffRS8QANACxge1tTSxgc7ypidneyJDs2zs4fwWVFVQJdeC7YNBZVsL6lhwbSMY+8cZNnD4pmcnsDC3Px+Pw+4Pyjz2AiMuMgwIkJDKAvwFBLVsSVrC5n1wDJG3P6aq+f0dsUYc5UxJsUYE26MSTfG/MsYU26MOccYM9r+WmHva4wx3zXGnGSMmWiMyfU7zmPGmFH25XG/7bnGmJPt29xi9IW0U7GRYTz29VOZO2Eo9/53M394a+tR7zstrW38edl2Ln70A8prm/jXdTk8uGAy8VGBWblraHwU918ykQ0FlTzybv8bhVVS3UhdUysjPDACA6ylVPMr6mize6X5ivKDPVKUV31TZ6t1GODWResPN8oNtvKaRr7++Cc89M425k9J4+VbZjFqSAcns9euBWOOvqxdG/yge1HvjNVTjsjdU0FNY4vrp4/4zMkewu/e3MryvBKunJ7pdDi9YtHqfCLDQrhwUorToXRoQU4Gdy7ZxMbCSialJzodjgogrxUwRITBsRE6AsMBS9YWcseLG6lvbgU+n9MLeL6xl3JOVHgoj159Cr94aROPLNvB6r0V7C6vY/+hBpLjIokMCyH/YD1fmpTCry8+OSiNx+ednMKCaek8unwHZ49JJmf4ifXX8BKvrEDik5kUQ2NLG6U1jQyNj/Lce5rqm26dO/aI90uAqLAQTh2RxCvri3hhTQFfnpzKd2ePYszQ4IyGz91TwS3PrqWiron7L5nIladmeOJEdm/SERgetiyvhIjQkF4fehkoY4fGkZoQxbt9ZBpJQ3Mrr6wrYu6EYSREB+YM0on68uRUIsNCWJirzTz7urKaJkRgYIw7n4sdGRQbGfAeGOpoDy7dekQyBu6b06u8KSw0hAcuncic7GQ+3FlB0aEGDNZogPyD9Vx7WhZ/vvqUoK6advdFE0gfGMOPFq6juqE5aPfrtD12AWOER6aQZNpx+hp5lh8egaEFDOUc3xKwEXYj3LTEaB64dBJPfXMG7982m2+fOZK3Nx/gvIdWcNPTq9lU2DvLSnfEGMM/Vuziyr+vIjI8hBdvOp2rpmf2u+IF6AgMT1u2tYQZI5MYEOmNP6OIMDt7CC+tLaSxpZXIsJ4vkeYm72w5QFVDCwty3NO8s72E6HAumJjCy+uKuPPC8ce1LJ3yhrKaRpJiIghzebd5f4NjIygNcAHjvksmBvT4XuTmOb3K+0SErcUdN858d0sJ914c3HhiI8N46IrJLPjrSu59dTMPLpgc3AAcsru8lvBQITUxyulQuiXTbynV6SOSDq9Q5ZW+Tqrvmj81jYnp1nLsJyV/3u9uSFwUd1wwju+cfRKPfbibJz7cwxubijknewi3zBnF1MyBvRZDZX0zty5az1ubDzB3wtCATr/zAu9kuuqwJWsLmXHfO+wqrWVDwSFPzV2OjgilrqmVsXe+6dl51z6LcgtISYji9JPcPQJmQU461Q0tvLnJ20smqa6VVXtvubnBsZEBn0JyUnLsEQlHf9baZvjb/3bSWSOHzub6KtVTbiuSTctK4ruzR7FodQFvbNzvSAzBtresjoykGM8UtdMSowkRq4AB1ntaiMDAGC1gKOd1lUsMHBDBT84bywe3z+En545h9b6DfOX/PuKaf37Mx7vKT/i+NxVW8uU/fcCyvBLuvHAcf71mWr8uXoAWMDzHN3f5QJVVma6sb3HlesQdWbK2kKdX7T183a1rKXdHcWUD728v5dJT0gkNcffQrZkjBpGZFKPTSPq48lrvLTc3KDaS8trGgDaZfWfzAd7ZfCBgx/eK/Io6rvrHKu5/I49JafFEhR/59h8dHsqtc8c6FJ3qazorhjlZJPv+OaOZlJ7AHS9t7FPNxDuyZG0h72w5wK7SWs+cLIoICyElIZp95dbUl7LaJpIGRLg+x1L9Q3dyiYTocL53zmg+vG0OP78gm7ziaq74+you/+tKVmwr7XGuY4zhmY/3cslfPqK5tY3nb5zJt84c2S+njLSnBQwPaG5tI7+ijk92V3DPq595du7yg0u30tDs7rWUu+vFtQW0Gbh0mnunj/iEhAgLpqXz0c5y9pXXHfsGypPKarw4AiOC5lZDVX1LwO7jH+/v4h/v7wrY8d3OGMPi1QWc/8f32VxUxe8XTOblW87ggUsmkZYYjWCd+bz/konawFP1mlvnjiW63ZRFp4tk4aEhPHTFFBqaW/npovWHV7voa6wTXRtosX8/L50sykyKOWIExqAB3npPU31XT3KJAZFh3HDWSXxw22x+9eXx7Kuo49rHPmH+/33EO5sPdKuQUdvYwg+fX8cvXtrEzJGDeO37ZzItq/80IT4WbzRP8Jglawt5cOlWig7Vk5oYza1zx3aaGLa0tnGgupHiynqKDjWwv7Ke/ZUN7Le/L6psoKymkWM9170wd7mzGAsP1fPK+iLOGz/UEz0afB8IcrIGemZ5ssty0vn929u48JH3qWlsOebzUnmPF6eQJMdZ8ZbWNJLgoeajXlFR28TPX9zIm58VM314Er+/fDIZ9jzz+VPT9P9fBYzvudXdXChYTkqO5c4Lx3Pnkk08uXIPX581wtF4elNDcys7SmrsE10dnyxy+vE/lqxBMbyzxTrLXV7bxOA4b40qVMpfVHgoX581gqtmZPLC6kL+770dfOvJXMalxPO9OaOYN2EYIR2MMNp+oJqbnlnDrtIafnLuGL47e1SH+/VnWsDoZR0tT3fbCxtYu+8gGUkxVnHCr0hRUt1A+5MAMRGhpCREkZoYzdhhcQxLiCY1IYqUxGhuXbSekuqjm955Ye5yamI0hR0UMUJF+P5/1hIfFcb8qWlcnpPBhNR41w2R8i9MGeDUU3uvOU+gfbyrghCB6kbrTLcum9i31De1UtvU6r0pJPbZtfKaRkYN0T4VvWn51hJ+tngDh+qauOP8bL515kgdiq2Cyq1Fsq/OyGRZXgn3v5HHrFGDGR2kpQ97izGGwkP15O2vZuuBarbsryKvuJrdZbW0djGqxAsnujKSYiiraaK2sYXymkZd/l31CZFhoVw9I5MFOem8sq6IR5fv4OZn1jBqSCy3zB5FW1sbv397O0WH6kmMCaemoYWEmHCe/uYMTvfISpPBpgWMXtbR8nSNLW38e6XV+yEqPITUhGhSEqOYNWowqYlRpNjXUxKs7+Ojwjr98P7zC8YdtR6x08Myu6ujtZSjw0O5b/7JJMdHsWh1Ps99ms+TK/cyLiWey3PSmT8lLajLrXWmfWEK4OV1RZw2crArE7T2Hly69ahCmVfOyKhj8y1FmuyxERi+s2tlNYFt5Nmf1DW1cN/rW3h61T7GDo3j39dPZ3xqvNNhKeUaIsJvL53EvIdX8IPn1rHku7OICAvujOrujtStamhmW3E1W4qr2VpcZRUtiqsPn4wASB8YTfaweOZNGEZ2Shz3vrrZsye6fCuR5B+so6zGe32dlOpKeGgIl05LZ/7UNF7fuJ8/L9vBD59fh8Dh5toH65oJEfjenFFavOiCFjB6WWcVbgHW3nUuCdHhJzSywK3DMrvjWLGfMXow99Y188qGIhbl5nPPq5u5//U8zh0/lAU56Zw5OtmxM4gdFaYamts8UwBwW0d41bt8BQyvDbf1TXkpC/BSqv3FuvxD/Oj5dewpr+XbZ47gJ+eN9cS0PKWCLTkukgcuncS3n8zlD29v4/bzs4N23x2N1L39xQ0cqKonNTGGvOIqthZXs2V/9RGjVuOiwsgeFsf8qWmMHRbHuJQ4xgyNI67dagQtrcazJ7qyBlkFjG0HaqhpbPHctEiluiM0RPjy5FQunJhCzm/eoaL2yJM4bQb+vmI3153ed6a49TYtYPSyzqZJpCZGk9hLS0G5dVhmdxwr9oSYcL42M4uvzcxiy/4qFuUW8NLaAl7buJ9h8VFcOi2NBdMyGB7g3hPNrW3sKq1l8/7Ko5IIf14pAHT2vDTAj55fxzUzszglM9F103ZU95TbIxi81vBsYEwEIWJNIQmUh66YErBju0VzaxuPLt/Bn5btYGhcJM98a4brl3dWymnnjh/KVdMz+NuKnXxhbDIzRw4Kyv12dkLk/jeshuahIcJJyQM4JWsgV8/IJHtYHNkp8aQmRHXrPdrLJ7p8IzDW7D0IWI2elXKDQOQSISHCwdqOR6B65fOFU7SA0cs6mybhhcq324xLieeuL4/n9vOzeXfLARbm5vOX93by6PKdTB+RxOU5GVwwcRgxESf2ND5U18Tm/VVs2W/NJd2yv4rtB2poarWaYEWEhhAeKjS3Hj231AtDMqHj52VkWAjTRyTxzuYDvLS2kPEp8XzttCwunpJ6wo+pCq7PR2B4q4ARGiIkDYigNIBTSLzyP3q8dpXW8KOF61mff4hLpqZx90UTSIjuZkPU/fvhyivh+edh2LDABqqUC9154XhW7iznJwvX88YPzyQ+KnDNhI0xfLrnYKcnRABe+/4ZjBoSS2TYiY2c8uqJrsSYCOKjwlibfwhAR2Ao1whULtHViW/VOf2U0su8XPl2q4iwEM6fmML5E1MormzghTUFLMrN56eL1vOrVz7jS5NSWJCTwSmZiby8rqjTx76tzbCnvPaIQsWW/VUUVX6+Hvzg2EjGpcRx/azhjEuJZ1xKPCOTB/Dahv2eLkx19bysbWzh5XVFPLlyD3e8uJH7XtvCpdPSuWZmljZW9AhfAWOQC/rF9NTg2MiATiF5dX0RAF+enBqw+3CCMYanP97Hb17bTGRYKI9efQoXTkrp2UF+/Wv44APr66OPBiZQpVxsQGQYD10xhcv+upK7X/4sIGdZS6sbeWFNAQs/zWdXWe0R8939pSVGMyE1odfv32syB8WwuagSgEFawFAuEahcQk98Hx/pzlq0/UFOTo7Jzc11OgzVTb4zGQtz83ltw37qm1sZEhdBRW3z4bXPAcJDhZzhA6lvamNrcfXhFwjfEE1fkcK6xDEkLqrT++zJ8rheZIxhzb6DPLVyL69vLKaptY3TTxrE12Zm8cXxQwkPDW6TM9U9S9YW8oslG6ltbCXNY8/LJWsLue2FDTS2tAUs9iv+thKA5288rVeP66SSqgZ+9sIG3ttaypmjB/PgZZMZltD5a9cRWlqgrQ3Ky2HkSGhogOho2LWr10ZhiMhqY0xOrxzMYzSX8KaH39nGw+9s55GrpnJRL3xAaWltY8X2Up77JJ9leSW0tBlysgZyxakZGGO4+5XNR31guf+SiZ557Q6km59ZzesbiwF4/2ezDy/9rJSTAplL9PXPFyeis3xCR2AoTxIRpo9IYvqIJH510QRe21DEL5d8dkTxAqC51bBqZwXTRyRx5fQMxqXEMz4lnlFDYnvc3M6rQzK7S0SYlpXEtKwk7vxSIwtz83lm1T5uemYNQ+MjuWp6JldNz2RofDc/KPWiQL+4e/XNo6NmcF5ZHtcXe2OLNVXLS7E76c1N1miwuqZW7r14Al+bmdX5vPjqanj9ddiyxbps3gzbtsHjj1sjL9qsx57WVh2Fofq1W2aP4r2tpdz50kZysgYe9/DtfeV1LMzNZ9HqfA5UNTJoQATfOGMEl+dkHDGiMSIs1JPvOcGQmfR5jzOdQqL6g77++SIQtIChPC82MowrTs3k9hc2drpPXzr7GgyDYyO5+QujuPGsk3hvawlPrdrLH9/dzp+W7WDuhKFcMzOL00YOCkrTz0B/SPdaEcAYw4GqRnaU1HD3K5uOagbnleVxO2pkV9/cygNv5HHxlFRtKAtH9KioShzEPa9s5oU1BUxMS+ChK6ZYH4iqqyEv7/MCxZYtcPHF8I1vQGWldXsRa7TFuHFw/vkwaJBVxGiye480NVnXf/lL7YWh+qWw0BAevmIKFzzyPj9dtJ6nvzmDkG6uetbQ3MrSz4p5/tN8PtpZTojA2WOSueeiDOZkD+1wiVb9wNI5XyPPARGhREfoKkpKqaNpAUP1GdoIp/eFhgjnjBvKOeOGsre8lmc/3sfzufm8vrGYk5IH8LWZWVwyLZ1lW0p69WxSa5uhqr6ZQ/XN/Oa1LR1+0P3VK59R19SKwWCM35xiYzDWF/tqu+v2Np9H3t3e4fF/+6azH6Rb2wz5FXXsKKlhR2kNO0pq2F5Sw66SGqobW7q8rRe6V3cWY3FVAyffvZSsQQMYPjjG+jrI93UAQ+Iiu/3BwsuWrC2k7aabmP/x+yy++AYe/cK1DCvcyT8GNzI7ZTxhQ2KhuRmSkqxpIQDh4TBmDNTbj21aGqxbZ22L9nsdvPnmz0df+OgoDNXPDR88gLu+NJ7bX9zIYx/u5ltnjuxy/81FVTz/6T6WrCuisr6Z9IHR/OTcMVyWk05KguYdxyu/og6A2qZWZj2wTEenKKWOoj0wbDpv1fvan0kHnVcaCA3Nrby2YT9PrdrLuvxDhIcIbVgfuH2iw0O57ysnM+/kFA7VN1FZ38yhOutiFSaaOFTXbG2vb6by8PfW9uqGrj+gB0tUeAjD4qMYEh/FsPgohsZHMjQ+iqHxUQxLiGJoXBRD4iO7nI50rOkpjS2t7CmzCxUlNWwvqWZHSQ27ymppavn8Q2ZyXCSjkmMZPTSWUUNiGZUcy48Xrqe4quGo+0xLjObD2+f07oPRy2Y9sKzDgmNCdDiXnJLG3vI69pTXkl9Rd8QKQFHhIWQlDSBrUAzDB1tFjeGDYsgaPICU+KgjihtL1hbys8UbaGoNTI+NQE09WrK2kN//+z3efvQbRLU0YYAjSjYXXACvvWZ9/5e/QEqKNbpi5EiriHEsU6dahY32pkyBtWtPOH7tgaG5hFcZY7jhqdUs23KAQbGRlFY3HvG/XdXQzCvrinj+03w2FlYSERrCvJOHccWpGZw2clC/KK4Gkn9fJB/N45Qb9MV+Wl7QWT6hBQybJh19g1d7GXjVxoJKrvj7SuqaWo+9czthIUJCdDgJMeEkRIeTGB1OYkyEtS06nER7+29e20J5B+tkD4uP4uVbZgH2hzsBsT/miVjbfKMn5PA2O7mUz/c576EV7K88ugiQEB3GgmkZHKhu5EBlAweqGyiubDgisfIZGBN+uLAxND7ycNFjT1ktT63ae8RtwkOFs8YkEyLCzpIa9lbUHS7+iED6wGhGJdtFiiGxjBoSx6jkWBJijv5g6uWiXXdjb20zFB2qZ095LXvK69hbZn8tr2VvRd0RRZ6IsBAyk2IYPiiG5tY2PtpZfkTxIzIshLu+NJ5LTkknMizkhD5s9OSxb2xp5VBdMwfrmqiobeJgbTMVdU0crLWv29sP1TVTUdtE0aF67l36KJdvfJvI1hZaEdakjuW5867l93dfBZmZEOLeprpawNBcwsueXrWHO5d8dsS2iLAQJqXFs6moiobmNrKHxXHlqRnMn5pGYoz3Vn9yq84K214oyqu+rcLOQ5M8uNqbl2kTT9Uv6LzS4JqYnkB9F8WL2+ZlHy5EJPoXK2IiGBAR2q3pGSEiHX5QvP387F5pKHrbvOwOj3/PRScf9VwyxlBV30JxVQMHqhoormqgxP56oKqRA1UN5BVXUVrdSFsnteHmVsO7W0oYNSSWscPiuHBSCqOGxHJSsnXpyZxfLy/b3N3YQ0OEjKQYMpJiOHP0kcdoazMUVzWwp7z28IiNvWXW163F1UctVdjY0sYvlmziF0s2AdZojqjwUKLDQ4k6fAk5fD26o20RoUSGhfDnZTs6nHp0x4sbWbKu0CpO1FnFipoupvzERYYxcEAEAwdEMDg2gtFDY3l/xUYWbHqXyFbrdqEYTi7ZxYoBGTB8eM8fbKVUt/3lvV1HbWtqaWP13kNcNSOTK0/NYGJagvbpCYDOphZ6YVqk6tu0cOEufbaAISLzgD8CocA/jTEPOBySUn1SZ71H0hKjuekLJ53w8QP9Ib0nxxcRqwgTE87YYXGdHrOltY3y2iZm3vfuUR+iwRr58c6Pz+61+L1QsOjIicYeEiKkJkaTmhjN6e2eaiNuf63T2902L5uG5lYamlupP/y17fC2huZWqhqaaWhuo76p9Yh9OytM+dQ3t1JR20RiTAQjBg9g4IAIkmKsAkXSgAgGxthfB4STGB3RYYO/F/9wF2KOHOkTYtq4PXcRcE13Hx6l1HHo6sPyfV+ZGMRKa19hAAAgAElEQVRI+h/tZabcalFuPgALcjIcjkRBHy1giEgo8ChwLlAAfCoirxhjNjsbmVJ9z61zx3Y4guHWuWN77T4C/SG9t48fFhrC0PgoTcYcFIjCmjGG5lZDfXMrcx9a0Wn/kVduOeO4ju8z59Cuw6MvfCJbWzjn0M4TOq5S6tj0dds5wcgnlDoei1cXAFrAcAv3TqI9MdOBHcaYXcaYJuA54GKHY1KqT5o/NY37L5lIWmI0gvUBzgs9GILh1rljiW7X4FOTseAIxGMvIkSEhZAQHc7t52cH7G+bmLeJJWsKmHX/u4y47b/Muv9dlqwpIDFv0wkfWynVNX3ddo7mE0qp7uiTIzCANCDf73oBMMOhWJTq87w8jSGQvNyjwut8j3GgViEJxtQmfZ4oFXz6uu0sfe1TSh1LXy1gdNRZ6aiZyyJyA3ADQGZmZqBjUkr1Q5qMOWf+1DT+88k+IDBLn+nfVqm+Sf+3lVLKvfrqFJICwH+SUjpQ1H4nY8zfjTE5xpic5OTkoAWnlFJKKaWUUkqpnumrIzA+BUaLyAigELgSuNrZkJRSSgXbE9dPdzoEpZRSSnmY5hLu0icLGMaYFhG5BViKtYzqY8aYzxwOSymlVJBFR4QeeyellFJKqU5oLuEufbKAAWCMeR143ek4lFJKOeeplXsA+Nppw50MQymllFIepbmEu/TVHhhKKaUU/92wn/9u2O90GEoppZTyKM0l3EULGEoppZRSSimllHI9LWAopZRSSimllFLK9bSAoZRSSimllFJKKdfTAoZSSimllFJKKaVcT4wxTsfgCiJSCuzt5cMOBsp6+ZjB5OX4NXZneDl28Hb8GrtzvBx/IGLPMsYk9/IxPSFAuQToc8wpXo4dvB2/xu4ML8cO3o5fYz9ah/mEFjACSERyjTE5TsdxvLwcv8buDC/HDt6OX2N3jpfj93Ls/YmX/04au3O8HL/G7gwvxw7ejl9j7z6dQqKUUkoppZRSSinX0wKGUkoppZRSSimlXE8LGIH1d6cDOEFejl9jd4aXYwdvx6+xO8fL8Xs59v7Ey38njd05Xo5fY3eGl2MHb8evsXeT9sBQSimllFJKKaWU6+kIDKWUUkoppZRSSrmeFjBOgIg8JiIlIrLJb9uDIpInIhtE5CURSfT72R0iskNEtorIXGeiPhxLt2MXkXNFZLWIbLS/znEu8p4/7vbPM0WkRkR+GvyIj3Qcz5tJIrJSRD6z/wZRzkTe4+dNuIj82455i4jc4VTcdjwdxf5rO+51IvKWiKTa20VEHrH/XzeIyCnORX441p7E/1V7+wYR+UhEJjsXec9i9/v5qSLSKiKXBT/iI+LoUewi8gV7+2ci8j9nov5cD583CSLyqoist+O/3rnI+w8v5xJ2PJpPOEBzCed4OZ/QXMI5Xs4nXJdLGGP0cpwX4CzgFGCT37bzgDD7+98Cv7W/Hw+sByKBEcBOINQjsU8FUu3vTwYKvfK4+/38BWAR8FOPPW/CgA3AZPv6IA89b64GnrO/jwH2AMNdFnu83/ffB/5qf38B8AYgwEzgY5c+bzqL/3RgoP39+U7H35PY7euhwDLgdeAyr8QOJAKbgUz7+hCPPW9+7vf/mwxUABFO/w59/dLD11VX5RLHEb/mE8487ppLBD5+T+QTPYxdcwnnHntX5RM9jD3guYSOwDgBxpgVWH8U/21vGWNa7KurgHT7+4uxXoAbjTG7gR3A9KAF205PYjfGrDXGFNnbPwOiRCQyaMG208PHHRGZD+zCit1xPYz/PGCDMWa9vV+5MaY1aMG208PYDTBARMKAaKAJqApWrO11Ert/PAOwYgbr//VJY1kFJIpISnAi7VhP4jfGfGSMOWhvP+L/wQk9fOwBvof1IaEk8NF1rYexXw28aIzZZ+/ntfgNECciAsTat2tBBZSXcwnQfMIpmks4x8v5hOYSzvFyPuG2XCKsNw+mjvIN4Hn7+zSsf36fAnubW/nH7u9SYK0xpjHI8fTE4dhFZABwG3Au4Pj0kW7yf+zHAEZElmJVMZ8zxvzOsciOzT/2xVhv3Puxzpr8yBhT0dkNnSIivwGuBSqB2fbmNCDfbzff/+v+4EZ3bJ3E7++bWGd/XKej2EUkDfgKMAc41bnoutbJ4z4GCBeR94A44I/GmCedibBrncT/Z+AVoAgr/iuMMW3ORKj8eDmXAM0nnKK5RJB5OZ/QXMI5Xs4nnMoldARGgIjIL7CqTc/4NnWwmyuXgOkgdt/2CVjD+m50Iq7u6CD2e4CHjDE1zkXVfR3EHwacAXzV/voVETnHofC61EHs04FWIBVrqPNPRGSkQ+F1yhjzC2NMBlbct9ibPfP/2kn8AIjIbKyk4zYnYjuWTmJ/GLjNybOD3dFJ7GHANOBCYC7wSxEZ41CIXeok/rnAOqz/2SnAn0Uk3qEQFd7OJUDzCadoLuEML+cTmks4x8v5hFO5hBYwAkBErgO+BHzVGON7kSoAMvx2S8eqTLlKJ7EjIunAS8C1xpidTsXXlU5inwH8TkT2AD8Efi4it3RyCEd18bz5nzGmzBhThzWPz/GGku11EvvVwJvGmGZ76NuHQI5TMXbDs1hnBMEj/6/t+MePiEwC/glcbIwpdyyq7vGPPQd4zv6fvQz4P3vYtlu1f968aYypNcaUASsAR5uedYN//NdjDVk1xpgdwG4g27HI+jkv5xKg+YRTNJdwBS/nE5pLOMfL+URQcwktYPQyEZmHVaG8yH6T8HkFuFJEIkVkBDAa+MSJGDvTWexidYJ+DbjDGPOhU/F1pbPYjTFnGmOGG2OGY1Vj7zPG/NmhMDvVxfNmKTBJRGLs+Z9nYzX1cY0uYt8HzBHLAKzmVXlOxNgZERntd/UiPo/vFeBaO/aZQKUxxlXDPaHz+EUkE3gR+JoxZpsTsR1LZ7EbY0b4/c8uBm42xixxIMROdfG8eRk4U0TCRCQG6wPPlmDHdyxdxL8POMfeZygwFmu+vwoyL+cSoPmEUzSXcI6X8wnNJZzj5XzC0VzCONyR1csX4D9Yc9iasSpl38RqqJWPNXRmHUd2w/0FVsfwrcD5XokduBOo9du+Dge74fb0cfe73a9wxyokPX3eXIPVMGwT8DuvxI7VuGeRHftm4FYXxv6C/bhuAF4F0ux9BXjU/n/dCOS49HnTWfz/BA76/U1yvRJ7u9s9gfOrkPQoduBW+/m+Cfihx543qcBb9nN+E3CN0/H3h8txvCe4JpfoafxoPuHk80ZzicDG74l8ooexay7hYPy4KJ/o4fMm4LmE2HeklFJKKaWUUkop5Vo6hUQppZRSSimllFKupwUMpZRSSimllFJKuZ4WMJRSSimllFJKKeV6WsBQSimllFJKKaWU62kBQymllFJKKaWUUq6nBQylVJ8gIu+JSI7TcSillFLKuzSfUMrdtIChlFJKKaWUUkop19MChlLKESLyMxH5vv39QyKyzP7+HBF5WkTOE5GVIrJGRBaJSKz982ki8j8RWS0iS0Ukpd1xQ0Tk3yLy/4L/WymllFIqmDSfUKp/0QKGUsopK4Az7e9zgFgRCQfOADYCdwJfNMacAuQCP7Z//ifgMmPMNOAx4Dd+xwwDngG2GWPuDM6voZRSSikHaT6hVD8S5nQASql+azUwTUTigEZgDVbicSbwCjAe+FBEACKAlcBY4GTgbXt7KLDf75h/AxYaY/yTEKWUUkr1XZpPKNWPaAFDKeUIY0yziOwBrgc+AjYAs4GTgN3A28aYq/xvIyITgc+MMad1ctiPgNki8ntjTEPAgldKKaWUK2g+oVT/olNIlFJOWgH81P76PvAdYB2wCpglIqMARCRGRMYAW4FkETnN3h4uIhP8jvcv4HVgkYhogVYppZTqHzSfUKqf0AKGUspJ7wMpwEpjzAGgAXjfGFMKfB34j4hswEpAso0xTcBlwG9FZD1WcnK6/wGNMX/AGj76lIjoa5xSSinV92k+oVQ/IcYYp2NQSimllFJKKaWU6pJWE5VSSimllFJKKeV6WsBQSimllFJKKaWU62kBQymllFJKKaWUUq6nBQyllFJKKaWUUkq5nhYwlFJKKaWUUkop5XpawFBKKaWUUkoppZTraQFDKaWUUkoppZRSrqcFDKWUUkoppZRSSrmeFjCUUkoppZRSSinlelrAUEoppZRSSimllOtpAUMppZRSSimllFKupwUMpZRSSimllFJKuZ4WMJRSniUiXxCRgv56/0oppZQ6cSJiRGRUf71/pbxECxhK9XMi8isReTqI9xchIotFZI/9hv2FLvbL83KBQESWi0ipiFSJyHoRudjpmJRSSqne5kAuMVNE3haRCvt9dpGIpPj9PFJE/ioiB+x9XhWRtGDF19tE5IsiskZEakUkX0QudzompZyiBQyl1AkRkbDjuNkHwDVAcRf73AqUHFdQnRCR0N48Xjf8AEgxxsQDNwBP+ydYSimllDquXGIg8HdgOJAFVAOP+/38B8BpwCQgFTgE/OmEA+W4854Tub/xwLPAL4AEYAqwOpgxKOUmWsBQqp8QkdtEpFBEqkVkq4icIyLzgJ8DV4hIjYist/dNFZFX7LMWO0Tk237H+ZU9guJpEakCvi4iISJyu4jsFJFyEVkoIkkdxWGMaTLGPGyM+QBo7STWEVgFjvu7+bv9XETK7FEdX/Xb/oSI/EVEXheRWmC2iFwoImvtURH5IvIrv/2H26NCrhORffYxf+H382j7mAdFZDNwaldxGWM2GGNafFeBcCCjO7+TUkop5TYuyiXeMMYsMsZUGWPqgD8Ds/x2GQEsNcYcMMY0AM8BE47x610gIrvs9/4HRSTEjvXrIvKhiDwkIhXAr0TkJBFZZsdZJiLPiEii3++3R0R+KiIbRKRSRJ4XkSi/n98qIvtFpEhEvnGMuO4E/mb/zi3GmHJjzM5j3EapPksLGEr1AyIyFrgFONUYEwfMBfYYY94E7gOeN8bEGmMm2zf5D1CAddbiMuA+ETnH75AXA4uBROAZ4PvAfOBs+zYHgUdPIOQ/YSVD9d3YdxgwGEgDrgP+bv++PlcDvwHisEZ+1ALX2rFfCNwkIvPbHfMMYCxwDnCXiIyzt98NnGRf5tr31yUR+a+INAAfA+8Bud34nZRSSilXcXkucRbwmd/1fwGz7CJKDPBV4I1jHOMrQA5wih2bf2FhBrALGIKVUwjWSZZUYBzWyYlftTve5cA8rGLKJODrAHbB56fAucBo4IvHiGumfbuNdtHj6c4KO0r1B1rAUKp/aAUigfEiEm6M2dNZ9V5EMrA+wN9mjGkwxqwD/gl8zW+3lcaYJcaYNmNMPXAj8AtjTIExphHrTfyy4xlmKSJfAcKMMS/14Ga/NMY0GmP+B7yGlTT4vGyM+dCOtcEY854xZqN9fQNWgnV2u+PdY4ypN8asB9YDvmTscuA3xpgKY0w+8MixAjPGfAmreHIB1tmgth78XkoppZRbuDKXEJFJwF1YU099tgH7gEKgCqvIcO8xfr/f2u/v+4CHgav8flZkjPmTPQKi3hizwxjztp17lAJ/4Ohc4hFjTJExpgJ4FWvqB1i5xOPGmE3GmFqOLny0l471uF2KVfCIppemwyjlRVrAUKofMMbsAH6I9SZZIiLPiUhqJ7unAhXGmGq/bXuxRjj45Le7TRbwkogcEpFDwBasRGdoT+IUkQHA74Dv9eBmB+0EwD9W/9/tiFhFZIZ83lyzEvgO1ggOf/69OeqAWPv71HbH29udAI0xzcaYN4C5InJRd26jlFJKuYkbcwmxVu54A/iBMeZ9vx/9BYgCBgEDgBc59giM9u/vXeUSQ+zfv9CeAvM0gcsl6rEKHtuMMTVYo10uOMZtlOqztIChVD9hjHnWGHMGVoJggN/6ftRu1yIgSUTi/LZlYp3FOHy4drfJB843xiT6XaKMMYX0zGishlzvi0gxVsKRIiLFIjK8k9sMtAsf/rEWdRHrs8ArQIYxJgH4K9ZQ0O7Yz5E9LDK7eTufMKzpJ0oppZTnuCmXEJEs4B3g18aYp9r9eDLwhD2iohFrxMJ0EWlfZPDX/v29q1zifnvbJLtR9zUELpfY0MH9K9VvaQFDqX5ARMaKyBwRiQQasKr5vgaaB4DhvmZV9tSIj4D7RSTKHpr5Taz5qZ35K/AbO5lARJKliyVDxVrezNfMKsK+HwE2Yb2pT7Ev37Ljm8LRZ2r83SPWsqtnAl8CFnWxbxzWWaEGEZmO1SOjuxYCd4jIQBFJp4uRIiKSLSLn240/w0XkGqw5uv/rwf0ppZRSruCmXEKsJVGXAY8aY/7awS6fAteKSIKIhAM3Y00DKevi/m+1398zsFYxeb6LfeOAGuCQHcutXezb3kKspqXj7f4cdx9j/8eB60VkpL3/bcB/e3B/SvUpWsBQqn+IBB4AyrCGNA7BapIJn3/YLxeRNfb3V2GNhCgCXgLuNsa83cXx/4g1quEtEakGVmE1vOrMVqzEJw1Yan+fZc8tLfZdgAqgzb7e4Yol9u9z0I71GeA7xpi8Lu77ZuBeO867sBKJ7roHa6jnbuAtoP0ZH3+CPcwWKMVKhq4wxqzp4jZKKaWUW7kpl/gWMBK4W6yVT2pEpMbv5z/FKrJsx3oPvgCrSWdXXsZannQdVj+tf3Wx7z1YzT4r7X1fPMaxD7OnlD6MVYDZYX/tav/HgCexmoHvBRqxGp4q1S+JMToiSSmllFJKKaWUUu6mIzCUUkoppZRSSinlelrAUEoppZRSSimllOtpAUMppZRSSimllFKupwUMpZRSSimllFJKuZ6rChj28kzr/C5VIvJDEUkSkbdFZLv9daC9v4jIIyKyQ0Q2iMgpfse6zt5/u4hc59xvpZRSSimllFJKqRPl2lVIRCQUKMRaPum7QIUx5gERuR0YaIy5TUQuAL6HtTTSDOCPxpgZIpIE5AI5gMFaEmmaMeZgZ/c3ePBgM3z48ID+TkoppYJrV2ktACOTBzgcSf+xevXqMmNMstNxOEFzCaWU6ns0l3BGZ/lEmBPBdNM5wE5jzF4RuRj4gr3938B7wG3AxcCTxqrCrBKRRBFJsfd92xhTASAibwPzgP90dmfDhw8nNzc3QL+KUkopJ1zxt5UAPH/jaQ5H0n+IyF6nY3CK5hJKKdX3aC7hjM7yCVdNIWnnSj4vOAw1xuwHsL8OsbenAfl+tymwt3W2XSmllFJKKaWUUh7kyhEYIhIBXATccaxdO9hmutje/n5uAG4AyMzM7GGUSiml3C4lIcrpEJRSSinlYZpLuIsrCxjA+cAaY8wB+/oBEUkxxuy3p4iU2NsLgAy/26UDRfb2L7Tb/l77OzHG/B34O0BOTo47m4EopZQ6bg9fOdXpEJRSSinlYZpLuItbCxhXcWS/ileA64AH7K8v+22/RUSew2riWWkXOZYC9/lWKwHO49ijOZRSSvVxzc3NFBQU0NDQ4HQonhcVFUV6ejrh4eFOh+Jq+pzrPfqcU0op5boChojEAOcCN/ptfgBYKCLfBPYBC+ztr2OtQLIDqAOuBzDGVIjIr/n/2bv3+CjLM//jnzvnwwRyRgiBIERAC4iCYlGromKrVmq1utuDPenWdm23u7Wt3fbX7Wmr6+/Xg922lq5u3dZWq0XU2pUKiGdFMKiVg5ySkAmSZDIJOUzO9++PeSYmECCHmXmeSb7v14sXmWeeeeYKjsk911z3dcGrznnfjTT0FBGRieM7j78FwLevPA2AmpoacnJyKCsrw5ihdhvKcFhrCQQC1NTUMGvWLLfD8TS95qJDrzkRccuRawlxl+cSGNbadqDgiGMBwlNJjjzXEh6xOtR17gXujUWMIiKSGLbXHh50u6OjQ28ko8AYQ0FBAfX19W6HMiRjTC7wX8B7CPfA+jSwC3gQKAMqgY9Ya4Mm/GL4KeEPRNqBT1prX3OucwPwTeey37fW3jfSWPSaiw6vv+ZEZPw6ci0h7vLyFBIREZGo0xvJ6PD4v+NPgSettfOARcAO4OvABmttObDBuQ3hvlvlzp+bgF8CGGPygW8T3qJ6FvDtAVtTR8Tj/1YJQ/+OIiKiBIaIiIhHbdq0iRdffHFM1/D5fFGKJjEYYyYB5wP3AFhru6y1TcBVQKSC4j5glfP1VcD/2LCXgVynYfhK4ClrbaO1Ngg8BVwWx2/FFXrNiYiIl3luC4m4b22FnzvX7aK2KcS03ExuXTmXVYtL3A5LRCTu3P55uGnTJnw+H+9973vj9pzjwMlAPfDfxphFwFbgS8AUa+1BAKfhd7FzfglwYMDja5xjxzo+SLRHsus1JyIi0eL275RYUAWGDLK2ws9ta97E3xTCAv6mELeteZO1FX63QxMRGbGTi7I5uSh7VI+N5c/DVatWceaZZ3LaaaexevVqAJ588knOOOMMFi1axIoVK6isrOTuu+/mxz/+MaeffjrPPfccn/zkJ3n44Yf7rxP5pLu1tZUVK1ZwxhlnsGDBAh599NEhn3eCSAHOAH5prV0MtPHudpGhDLUvwR7n+OAD1q621i6x1i4pKioaTbz99JoTEfGesawl3DRe39epAkMGuXPdLkLdvYOOhbp7uXPdroTP1onIxPPDqxce877vPP7WcRtzVVQ30dXbN+hYqLuXrz78Bn/YXD3kY06dNmlYXcrvvfde8vPzCYVCLF26lKuuuoobb7yRZ599llmzZtHY2Eh+fj6f+9zn8Pl8fOUrXwHgnnvuGfJ6GRkZPPLII0yaNImGhgaWLVvGBz/4wYnaM6AGqLHWvuLcfphwAuOQMWaqU30xFagbcH7pgMdPB2qd4xcccXzTWALTa05EJPEcby3hZeP1fZ0qMGSQ2qbQiI6LiIxXR76RPNHxkbjrrrtYtGgRy5Yt48CBA6xevZrzzz+/fzxkfn7+iK5nreUb3/gGCxcu5OKLL8bv93Po0KExx5mIrLXvAAeMMXOdQyuA7cBjwA3OsRuASMnAY8AnTNgyoNnZarIOuNQYk+c077zUORYzes2JiEi0jNf3darAkEGm5WbiH+JFPS0304VoRETG5rY1bwBDf3pyok+tl9++ccifhyW5mTz4D+eMOqZNmzaxfv16XnrpJbKysrjgggtYtGgRu3btOuFjU1JS6OsLv5m11tLV1QXA/fffT319PVu3biU1NZWysjI6OjpGHeM4cAtwvzEmDdgHfIrwhzZ/NMZ8BqgGrnXO/QvhEap7CI9R/RSAtbbRGPM94FXnvO9aaxvHEpRecyIiied4awkvG6/v61SBIYPcunIuKUmDyz8zU5O5deXcYzxCRMS79tW3sa++bVSPvXXlXDJTkwcdi8bPw+bmZvLy8sjKymLnzp28/PLLdHZ28swzz7B//34AGhvD75NzcnJoaWnpf2xZWRlbt24F4NFHH6W7u7v/msXFxaSmpvL0009TVVU1phgTnbV2m9OXYqG1dpW1NmitDVhrV1hry52/G51zrbX2C9ba2dbaBdbaLQOuc6+1do7z579jHbdecyIi3jOWtYSbbl05l9Tk8fe+TgkMGWTV4hLmT83pv12Sm8kPr16Q0PukRERGY9XiEn549QJKcjMxRO/n4WWXXUZPTw8LFy7kW9/6FsuWLaOoqIjVq1dz9dVXs2jRIq677joArrzySh555JH+hoo33ngjzzzzDGeddRavvPIK2dnhpmIf/ehH2bJlC0uWLOH+++9n3rx5Y/32xQV6zYmISLSsWlzCytOm9N8eL+/rjLVHNdSekJYsWWK3bNly4hMngMt+8iw732mhND+T5756kdvhiIiM2nW/egmgv/x+x44dzJ8/382QxpWh/j2NMVuttUtcCslVQ60l9JqLLv17iki8HbmWSCT/+sib3P9KNYtKc3n0C8vdDmdEjrWeUAWGDGKtpSrQjjHgD4bo6hl74zARERERERGJr0gPjEBrp8uRRI8SGDJIfUsnoe5eTi/Npc/CgWC72yGJiIzaqdMmceq0SW6HISIiIgkqkdcSNcFwAqOhtZPxsvNCU0hkkMpAOGHxvlOKqKhuorKhjdlFPpejEhEZnRNNfRARERE5nkRdS1hr8QdDJCcZOrr7aO/qJTs98d/+qwJDBqkMhDvsvu+UIue2KjBEREREREQSSWNbF6HuXuZOCQ9oCLR2uRxRdCiBIYNUBdpISTIsKJnMpIwUKhsSb2SQiEjEPz1QwT89UOF2GCIiIpKgEnUtEel/sag0F4D6cdIHQwkMGaQy0M70vExSkpMoK8zur8gQEUlEB5s7ONjc4XYYIiIikqASdS3hd/pfnF46GRg/jTyVwJBBqgJtzCwIz3gvK1ACQ0TE63y+cJ+i2tparrnmmuOe+5Of/IT29pFtDdy0aRNXXHHFqOOT8UevORER74s08Fw4PVyBEWjTFhIZZ6y1VDW0U1aQBUBZQZZGqYqIHDwI73sfvPNO3J6yt7d3xI+ZNm0aDz/88HHPGc2bSXGBXnMiIjJG/qYQOekpzCoMfzjd0KIKDBlnGtu6aOnsebcCozBbo1RFRL73PXj++fDfUVBZWcm8efO44YYbWLhwIddccw3t7e2UlZXx3e9+l3PPPZeHHnqIvXv3ctlll3HmmWdy3nnnsXPnTgD279/POeecw9KlS/nWt7416Lrvec97gPCb0a985SssWLCAhQsX8rOf/Yy77rqL2tpaLrzwQi688EIA/vrXv3LOOedwxhlncO2119La2grAk08+ybx58zj33HNZs2ZNVL5vGQG95qLyfYuITGQ1wRAleZlkpCaTk5EybiowEn+OikRNZOJIWaFTgeFk6zRKVUQS1Rkz845/wgUXHH3sIx+Bz38e2tthxQrYvBn6+uDuu6GiAm66CT75SWhogCPL5zdtGlZcu3bt4p577mH58uV8+tOf5he/+AUAGRkZPP/88wCsWLGCu+++m/Lycl555RU+//nPs3HjRr70pS9x880384lPfIKf//znQ15/9erV7N+/n4qKClJSUmhsbCQ/P6/BHJwAACAASURBVJ8f/ehHPP300xQWFtLQ0MD3v/991q9fT3Z2NnfccQc/+tGP+OpXv8qNN97Ixo0bmTNnDtddd92wvicZJr3m9JoTkYRywrWER9UE2ynJzQSg0JdOwzjpgaEEhvSrcvpdDOyBARqlKiKJ62uXzRvbBaqqwNrw19aGb0dBaWkpy5cvB+BjH/sYd911F0D/G7fW1lZefPFFrr322v7HdHaGFx4vvPACf/rTnwD4+Mc/zte+9rWjrr9+/Xo+97nPkZIS/jWfn59/1Dkvv/wy27dv74+jq6uLc845h507dzJr1izKy8v741u9enVUvm8ZBr3m9JoTEU8Z81rCJf6mEGfPCv8sLshOGzdjVJXAkH6VgXaSDEzPC2fq8rJSNUpVRMa343163dwMweDgN5PBIFx2Wfh2YeGwP/0+kjFmyNvZ2eHEcV9fH7m5uWzbtm1Yjz+StXZY51xyySX84Q9/GHR827ZtJ3ysjIFec3rNiYjEWHOom5aOHkqc93UFvjT21Y+P93TqgSH9qgJtTMvNJD0lGQgvVjRKVUQS2ed+u5XP/Xbr6B78ve+Fy/gH6u2NSl+C6upqXnrpJQD+8Ic/cO655w66f9KkScyaNYuHHnoICL/xe/311wFYvnw5DzzwAAD333//kNe/9NJLufvuu+np6QGgsbERgJycHFpaWgBYtmwZL7zwAnv27AGgvb2dt99+m3nz5rF//3727t3bH5/EiV5z/fGJiHjFmNYSLomMUJ2eF24NUOhLHzc9MJTAkH5Vgfb+bSMRGqUqIoks2N5FsH2Uv7Bfegm6jnhsVxe8+OKY45o/fz733XcfCxcupLGxkZtvvvmoc+6//37uueceFi1axGmnncajjz4KwE9/+lN+/vOfs3TpUpqbm4e8/mc/+1lmzJjBwoULWbRoEb///e8BuOmmm3j/+9/PhRdeSFFREb/5zW/4u7/7OxYuXMiyZcvYuXMnGRkZrF69mssvv5xzzz2XmTNnjvn7lWHSa06vORHxnDGtJVzibwonMCI9MAp86QTbu+jpTfzpksZGyhQnuCVLltgtW7a4HYarFn/3r3xgwVR+8KEF/cd+9NTb/OfG3ez83vtJS1G+S0QSy3W/Cn/i/OA/nAPAjh07mD9/vpshUVlZyRVXXMHf/vY3V+OIhqH+PY0xW621S1wKyVVDrSX0mosuL/x7isjEcuRaIhH85oX9/Nvj29nyzYsp9KXzPy9V8n8efYvN/7qC4pwMt8MblmOtJ/SOVABobu8m2N49RAVGlkapioiIiIiIJIiaYIiM1CQKstOA8BYSYFw08lQCQwCoagxvE5lRkDXo+MBRqiIiMnZlZWXj4pNwSRx6zYmITCz+phAluZn9DZIjiYzxkMDQFBIB3h2VemQFxizn9n4lMEQkAS2fU+h2CCIiIpLAEnEtURMMUZL37gfTBU4FRkNrp1shRY0SGAJAlZOgmJE/uAIj1xmlWhXQFhIRSTxfXFF+1LHhjHuUE1MPreHTay469JoTETcMtZbwOn9TiAXTJ/ffLhpHCQxtIREgXIFx0qQMMtOSBx03xjBLo1RFZJzIyMggEAjojdAYWWsJBAJkZCRGIzA36TUXHXrNiYgMT3tXD41tXf0TSAAmZaaQkmTGxShVVWAIAFWBNmYe0f8iYmZBNhUHgnGOSERk7G64dzMA9336LACmT59OTU0N9fX1boY1LmRkZDB9+nS3w/A8veaiR685EXHDkWsJr/MHwyNUp+e9m8AwxlDgSyMwDiowlMAQIFyBsWJe8ZD3lRVm8+c3aunq6dMoVRFJKB3dvYNup6amMmvWLJeikYlIrzkRkcR25FrC62qajk5gABRkp9MwDpp46t2o0NrZQ0NrJzMLh67AmFUYHqVa3ag+GCIiIiIiIl4VqcAoyR383q4wJ31cVGAogSFUOf0tjpxAEjHTOV6lPhgiIiIiIiKeVRMMkZpsKM5JH3S8MDtNFRgyPkQmjByrB4ZGqYqIiIiIiHifvynEtNxMkpIGT78q8KXR0NqZ8E2l1QND+ieMzDxGBUZklKomkYhIolkxf+jePiIiIiLDkWhrCX+wfdAEkohCXzqdPX20dfXiS0/cNEDiRi5RU9XQTqEv/Zgv5Mgo1UilhohIorjp/NluhyAiIiIJLNHWEjXBEBfMLTrqeIEvvKUk0NqZ0AkMbSERqhrbKDvG9pGIssJsbSERERERERHxqM6eXupaOo9q4AnhLSQADQneyFMJDKEq0H7M7SMRMwuyqW0K0dmTWGOERGRiu+5XL3Hdr15yOwwRERFJUIm0lqht6gCgJO/oLSRFTgVGojfyVAJjguvo7uVgc8cxG3hGREapHmgMxSkyERERERERGa7ICNXpQyQwIhUYASUwossYk2uMedgYs9MYs8MYc44xJt8Y85QxZrfzd55zrjHG3GWM2WOMecMYc8aA69zgnL/bGHODe9+Rt1U3Hn8CSUSkQqNS20hEREREREQ8x98Ufm83VBPP/OxIAkNbSKLtp8CT1tp5wCJgB/B1YIO1thzY4NwGeD9Q7vy5CfglgDEmH/g2cDZwFvDtSNJDBoskJMpOsIUkMkpVk0hERERERES8pyYYIsnASZMzjrovPSWZnIwU9cCIJmPMJOB84B4Aa22XtbYJuAq4zzntPmCV8/VVwP/YsJeBXGPMVGAl8JS1ttFaGwSeAi6L47eSMCKTRU6UwMjLTmNyZqoSGCIiIiIiIh7kD4aYOjmT1OSh3+YX+dJpaEvsLSRem59yMlAP/LcxZhGwFfgSMMVaexDAWnvQGBMZxlsCHBjw+Brn2LGOyxEqA23kZqUyOSv1hOeWFWRplKqIJJQrFk51OwQRERFJYIm0lqhpCg25fSSiwJeW8FtIvJbASAHOAG6x1r5ijPkp724XGYoZ4pg9zvHBDzbmJsJbT5gxY8bIox0HhjOBJKKsMJutVcEYRyQiEj0fP6fM7RBEREQkgSXSWsIfDHHWrPxj3l+Qnc6e+tY4RhR9ntpCQrhSosZa+4pz+2HCCY1DztYQnL/rBpxfOuDx04Ha4xwfxFq72lq7xFq7pKioKKrfSKKoDLRRdoIGnhEapSoiiSbU1UuoSz+zRMabtRV+lt++kVlff4Llt29kbYXf7ZBEZJxKlLVET28f7xzuGHICSURhTuJXYHgqgWGtfQc4YIyZ6xxaAWwHHgMik0RuAB51vn4M+IQzjWQZ0OxsNVkHXGqMyXOad17qHJMBOnt6qW0KDbsCQ6NURSTRfPK/N/PJ/97sdhgiEkVrK/zctuZN/E0hLOBvCnHbmjeVxBCRmEiUtcQ7hzvo7bPH30KSnU6wvZue3r44RhZdXttCAnALcL8xJg3YB3yKcKLlj8aYzwDVwLXOuX8BPgDsAdqdc7HWNhpjvge86pz3XWttY/y+hcRQEwzRZxl2BUbZgFGqc4p9sQxNREREZEh3rttFqHvwp6Gh7l7uXLeLVYvV8kxEJqaaYPhD5pLjVWD4wqNUG9u6KJ509KSSROC5BIa1dhuwZIi7VgxxrgW+cIzr3AvcG93oxpcqZ6LIsHtgaJSqiIiIuKy2aehK0GMdFxGZCPxOAmN63rE/nC70pQPQ0Jq4CQxPbSGR+KpsiIxQHV4FhkapioiIiNumHaM8+ljHRUQmAr+TxJ06+diJiQIngRFoS9w+GEpgTGBVgTZy0lPIz04b9mPKCrP7Ex8iIiIi8XbLRXOOOpaZmsytK+cOcbaIyMRQE2ynKCedjNTkY55T4GwhCbR2xSusqPPcFhKJn6rGdmYWZmHMUFNnh1ZWkKVRqiKSMK45c7rbIYhIlE3OTAXCe7kbWrswwHc+eJr6X4hITCTKWsLfFDruBBKAwuzIFpLErcBQAmMCqwq0c+q0SSN6TFlBNo+/XktnTy/pKcfO7omIeMG1S0pPfJKIJJT1O+qYnJnKS7etYGtVkOtXv0xy0vA/jBERGYlEWUvUBEMsKJl83HMmZaaQmmxoSOAKDG0hmaB6evs40NjOzPzh9b+IKNMoVRFJII1tXTS2Je4vaREZrLfP8vSuOi6YW0RqchJnz8pnVmE2D756wO3QRGScSoS1RF+f5WBTx3EbeAIYYyjITieQwBUYSmBMULVNHfT02f7JIsM1cJSqiIjX3fy7rdz8u61uhyEiUbLtQJDGti5WzJ8ChBfj1y0tZXNlI3vqWl2OTkTGo0RYS9S3dtLV23fcEaoRBb60hN5CogTGBFXZP0J1ZBUYswo1SlVERETcsX5HHSlJhvedUtR/7MNnTCclyfDHLarCEJGJqSYYHrIwfRjTmAp96QQ8XlFyPEpgTFBVTgKirHBkFRi5WRqlKiIiIu5Yv/0QS8vy+xt5AhTlpHPx/Cn8aWsNXT19LkYnIuKOmmB4e/+JmnhCuAIjkaeQKIExQVUG2slITaI4J33Ej9UoVREREYm36kA7u+taWTG/+Kj7rjurlEBbF+t3HHIhspFZW+Fn+e0bmfX1J1h++0bWVvjdDklEEpy/KZzAGM4WkkJfOvWtnVhrYx1WTCiBMUFVBdooK8ge0QjViLKCLFVgiIiISFxFkhOXnDrlqPvOLy9i2uQMHvB4M8+1FX5uW/Mm/qYQlvCbjtvWvKkkhoiMSU0wRF5WKllpJx4yWuhLo6unj9bOnjhEFn1KYExQlYH2Efe/iCgryKa2KURnT2+UoxIRia6PLZvJx5bNdDsMEYmCDTsPMafYx8whGpAnJxmuXVLKc7vr+/eCe9Gd63YR6h68fgp193Lnul0uRSQiJ5IIawl/MHTCCSQRBdnhCvxE3UaiBMYE1NtnqQ60j3gCScSswmxnlKp3FwgiIgBXLprGlYumuR2GiIzR4Y5uXtnXOOT2kYhrl0wH4I9bauIV1ojVNg09hv5Yx0XEfYmwlvA3hSgZRgNPCPfAAAi0JeYkEiUwJqB3DnfQ1ds35CcYwxGp3FAfDBHxutqmkN4YTEDGmEpjzJvGmG3GmC3OsXxjzFPGmN3O33nOcWOMucsYs8cY84Yx5owB17nBOX+3MeYGt74fgWd21dPTZ7l4/tHbRyKm52VxXnkRD205QG+fN/d2T8vNGPJ4dnoy3b1qQCriRV5fS1hrqQm2D6v/BYR7YADUt6gCQxJEVYMzgWSUW0g0SlVEEsWXH9zGlx/c5nYY4o4LrbWnW2uXOLe/Dmyw1pYDG5zbAO8Hyp0/NwG/hHDCA/g2cDZwFvDtSNJD4m/DjkPkZaVyxozj/yf4u6WlHGzu4Nnd9XGKbGTev2DqUceSkwytnb1cv/pl3mnucCEqETker68lGtu66OjuG9YEEng3gaEKDEkYlYFw5cTMEY5QjdAoVRERSUBXAfc5X98HrBpw/H9s2MtArjFmKrASeMpa22itDQJPAZfFO2iBnt4+nt5Vz4XziklOOn7z8RXzp1CQncYDm6vjFN3wWWt5dX8jeVmpTMvNwAAluZn8v2sXcdffLWbHwcNcftdzPL+7we1QRSSB9E8gGeYWkvxsZwtJgvbAOHGbUhl3qhrbSEtJYuqkocsYh0OjVEVExMMs8FdjjAV+Za1dDUyx1h4EsNYeNMZEmimUAANHV9Q4x451XOJsa1WQ5lD3cbePRKSlJPHhM6dz7/P7qWvpoDhn9GudaHt2dwOv1zTz7x9awN+fPeOo+0+dmsPNv3uNj9/7Cv+04hRuuWgOSSdI2IiI1ASHP0IVwj8nJ2Wk0NCqCgxJEFUN7czIzxrTL8VZBVnsb1AFhoiIeNJya+0ZhLeHfMEYc/5xzh3ql6E9zvHBDzbmJmPMFmPMlvp6b25bSHQbdtaRmmw4r7xwWOdft7SUnj7Lmte8M5rUWstdG3YzbXIGHz5z6DzYnOIcHv3H5Vy1aBo/Xv82n/zNqzS2JeYnpCISP34ngTHcKSQAhTnpCVuBoQTGBFQZaBt1/4uImQXZ1DZrlKqIiHiPtbbW+bsOeIRwD4tDztYQnL/rnNNrgNIBD58O1B7n+JHPtdpau8Rau6SoqCja34oA63ccYtnJBeRkpA7r/NlFPs4qy+fBVw9grTeaeb60N8DWqiCfu2A26SnJxzwvKy2FH193Oj/40Ht4eW+Ay+96jteqg3GMVEQSTU2wnZz0FCZnDu9nJEBhdroqMCQxWGupCrQzI390/S8iZhVmYzVKVUQ87sbzTubG8052OwyJI2NMtjEmJ/I1cCnwN+AxIDJJ5AbgUefrx4BPONNIlgHNzlaTdcClxpg8p3nnpc4xiaN99a3sq29jxbxjj08dynVLS9nf0MYr+xtjFNnI3LVxN8U56XxkSekJzzXG8NGzZ/Knm99LcpLhul+9xG9e2O+ZZIzIROP1tYS/KTTs7SMRBb40JTAkMdS3dBLq7qWscKwVGBqlKiLed/GpU7j41BPvm5dxZQrwvDHmdWAz8IS19kngduASY8xu4BLnNsBfgH3AHuDXwOcBrLWNwPeAV50/33WOSRxt2BEulFkxjP4XA31gwVRyMlI80cxz8/5GXt7XyD+8bzYZqceuvjjSgumTeeKW83jfKUX82+Pb+cffV9DS0R3DSEVkKF5fS9QEQ8OeQBJR6EsnkKBb1NTEc4Lpn0BSMPYKjPD11AdDRLxrb30rEC4pl4nBWrsPWDTE8QCwYojjFvjCMa51L3BvtGOU4Vu/4xDzTsqhNH9kH7xkpiWz6vQSHtxygO+0dzM5a/il1dH2s427KfSl8fdnHd2480QmZ6Wy+uNLWP3cPv7jyZ3sOHiYX3zsDOadNCkGkYrIULy+lvAHQ5w9K39EjynwpdHU3k13bx+pyYlV05BY0cqYRRIOY+2BkZuVRm5Wqhp5ioinfWPNm3xjzZtuhyEio9Dc3s2WqiAr5o9s+0jE9WeV0tXTx9pt7jXzfK06yHO7G7jxvJPJTBt+9cVASUmGz71vNr+/cRmHO3pY9fMX+NPWmihHKiLH4uW1RHOom5bOnhE18AQo8KUDEEzAKgwlMCaYqkAbKUlm2HOCj2dmQTZVAW0hERERkejb9HYdvX12xNtHIk6bNpkFJZP5w+Zq1/pH/GzDbvKyUvnYspljvtaykwv4yxfPZdH0XP7lode5bc0bdHSrmbrIROYf4QjViMLsNADqE7APhhIYE0xloJ3peZmkRKFUSKNURUREJFae2n6IQl8ap0/PHfU1rltays53WnijpjmKkQ3PmzXNPL2rns+edzLZ6dHZtV08KYP7P3s2N18wmz9sPsCHf/kiVdrOKzJh1QTDHyaP9MPpwpxwBUYijlJVAmOCqQq0jbn/RYRGqYqIiEgsdPf28czb9Vw0r5ikJDPq61x1+jQyU5N54NUDUYxueO7auJtJGSl84pyxV18MlJKcxNcum8c9NyzhQGM7V/zsef761jtRfQ4RSQz+pnAFxkibeBY4FRiBNlVgiIdZa6lqaB9z/4sIjVIVERGRWHh1fyMtHT2j3j4SkZORyuULp/LYNj9tnT1Riu7Ettce5qnth/j0ubPIyYhNA9EV86fwxBfPo6wgm5t+u5Uf/mUH3b19MXkuEfEmfzBERmoS+U5CYrgiPTAaWhKvAkNTSCaQxrYuWjp7olaBUeZMItnf0M6c4pyoXFNEJJpuuajc7RBEZBTW76gjLSWJ88oLx3yt65eW8vDWGp544yAfWVoahehO7D+f3o0vPYVPvXdWTJ+nND+Lhz53Dt9/Yju/enYfFdVNXL7wJFY/u5/aphDTcjO5deVcVi0uiWkcIuOZl9cSNcEQJbmZGDOySrVJGSmkJSfRkIAVGEpgTCCREaplhdGpwIhUcmjvpYh41blRePMjIvFlrWXDzkO8d3YBWWljX6qeOTOPOcU+Hni1Oi4JjN2HWvjfv73D5y+YHZfxrRmpyXx/1QKWzMzn1odeZ3NlY/99/qYQtznTE5TEEBkdL68l/E2hEU8gATDGUOBLUw8M8bZIoiFaFRgapSoiXvdWbTNv1ca/eZ+IjN7e+laqAu1j3j4SYYzh+qWlvFbdxNuHWqJyzeP5z6f3kJmazGfOPTnmzzXQqsUl5A1RRh7q7uXOdbviGovIeOLltURNsH3EE0giCnxpNGgKiXhZVaCdJDPyJi/HU6ZRqiLiYd99fDvffXy722GIyAg8tb0OgBXziqN2zQ8tLiE12fDA5tg289xX38rjr9fy8WUzR7wnPRrqW4Z+M1LrNPoTkZHz6lqivauHYHv3iCeQRBT60lWBId5WFWhjWm4m6SnJUbtmmUapioiISBRt2HGI06ZNYtooF+VDKfClc+lpJ7Gmoiam09N+/vRe0lKS+Ox58a2+iDjWv1k0/y1FxBv8wdFNIIkoyE4noAoM8bLKQDtlUdo+ElFWGB6l2tGtUaoiIiIyNo1tXbxWHYza9pGBrl9aSlN7N+veOhT1awNUB9pZu83P3581k6Kc9Jg8x4ncunIumamDP6jKTE3m1pVzXYlHRGKnZowJjEJfGg1tXVhroxlWzCmBMYFUBdqYEaURqhFlBeFRqjVBbSMRERGRsXl6Zx19Fi6eH73tIxHLZxcyPS+TB1+tjvq1AX6xaQ/JSYZ/eJ871RcQ7oPxw6sXkJ0WTmKU5Gbww6sXqIGnyDhU0xRJYIzu/V2BL42unj5a4jhiOhqUwJggmtu7CbZ3908OiZaBo1RFRERExmLDzkMU56TznmmTo37tpCTDdUtKeWFPgOoo9++qCbbz8NYarl9aypRJGVG99kitWlzC198/D4CHb36vkhci45Q/GCItOYki3+gqvgqdxyVaHwwlMCaIqsboTiCJmOVcr1J9METEg7562Vy+eplKp0USQWdPL8/sqmfF/GKSkkxMnuOaJdNJMvDgluhWYdz9zF6Mgc+9b3ZUrztac4pzANh9qNXlSEQSn1fXEjXBdqbmZoz652VBfwIjsfpgKIExQVQ6nzREuwfG5KxUcrNSqQwogSEi3nPmzHzOnJnvdhgiMgyv7GukrauXi2PQ/yJi6uRMLphbzENbaujp7YvKNd9p7uCPr9ZwzZmlnmmWWT7FB8CeOiUwRMbKq2sJf1NoTNMlC5xJSYk2SlUJjAmiyqmQmJEf3S0kEE6KKIEhIl60taqRrVWNbochIsOwYcchMlKTWD6nMKbPc/3SUupaOtm0qz4q17v7mb30WsvnL/BG9QWE35jkZaWyWwkMkTHz6lrCHwyNeoQq0N9suEFbSMSLKgPtnDQpg8y06I1QjSgryKJSPTBExIP+48ld/MeTu9wOQ0ROwFrL+h11nDunkIzU6K9VBrpwXjFFOek8EIVmnnUtHfxhczVXLy6hNAYfEo2WMYY5xT721LW4HYpIwvPiWqKju5e6lk5Kckf/cycvK1yBoR4Y4klVgTZmRrmBZ4RGqYqIiMhY7DrUgr8pFJPxqUdKTU7i2jOns3FnHe80d4zpWr9+dh/dvX184cI5UYoueuYU5/D2odaEG5EoIid20PnZNZYtJGkpSUzOTNUWEvGmykB71PtfRMwqDI9SPdCoKgwREREZuQ076gBYMS/641OH8pElpfRZ+NNrNaO+RqC1k9+9XM1Vp5f0T2XzkvJiH82h7oQrDxeRE/MHwyNUS8aQwAAo9KURaFMCY0yMMZXGmDeNMduMMVucY/nGmKeMMbudv/Oc48YYc5cxZo8x5g1jzBkDrnODc/5uY8wNbn0/XtDa2UNDayczC2NTgRGZbFIZ5ZFkIiIiMjE8tf0Qi6ZPpjhOI0jLCrM55+QCHni1mr6+0VUo3PP8fjp6ej1ZfQFq5CkyntUEw++7xtIDA8KTSBItyem5BIbjQmvt6dbaJc7trwMbrLXlwAbnNsD7gXLnz03ALyGc8AC+DZwNnAV8O5L0mIiqnAabMavA0ChVERERGaX6lk5er2mKy/aRga4/q5QDjSFe2hcY8WOb2ru478VKPrBgKnOKfTGIbuzKnVGq6oMhMv74m0IkJxmmTh5b0rfQl5ZwY1RT3A5gmK4CLnC+vg/YBHzNOf4/Nry572VjTK4xZqpz7lPW2kYAY8xTwGXAH+IbtjdUOZURseqBoVGqIuJV/+fKU90OQURO4OmddVgLK+bHZ/tIxMrTTmJyZioPvHpgxJNP7n2hkrauXm65yJvVFwBTJqWTk56iSSQiY+TFtURNMMRJkzJISR5bPUJBdjoNrSNP4rrJiwkMC/zVGGOBX1lrVwNTrLUHAay1B40xkd9wJcCBAY+tcY4d6/ggxpibCFduMGPGjGh/H57xbgIjdvszNUpVRLzotGmT3Q5BRE5g/Y5DTJucwalTJ8X1eTNSk/nQ4hJ+/0o1jW1d5GenDetxhzu6+e8X9rPytCnMOym+MY+EMYbZxT52H1ICQ2QsvLiWGOsI1YhCXzrNoW66evpIS/Hq5ozBvBjlcmvtGYS3h3zBGHP+cc41Qxyzxzk++IC1q621S6y1S4qKikYXbQKoCrRR6EvHlx67fNWswmyNUhURz3l+dwPP725wOwwROYaO7l6e293ARfOLMWao5VtsXX9WKV29fTxS4R/2Y+57oZKWjh5uuag8hpFFR3mxTxUYImPkxbWEvyk0pgkkEQW+cOI22J44fTA8l8Cw1tY6f9cBjxDuYXHI2RqC83edc3oNUDrg4dOB2uMcn5AqA22UxWj7SMTMgiyNUhURz/nZxt38bONut8MQkWN4aW+AUHcvF8e5/0XEvJMmcXppLg++Wj2scaOtnT3c88J+Vswr5j0l3vtU9kjlU3w0tHbSlEBvTkS8xmtrie7ePg42h8Y8gQTCPTAg3IsoUXgqgWGMyTbG5ES+Bi4F/gY8BkQmidwAPOp8/RjwCWcayTKg2dlqsg641BiT5zTvvNQ5NiFVBdqZEeMEhkapioiIyEit33GIrLRklp1c4FoM1y8t5e1DrbxW3XTCc3/7UhVN7d3cssL71RcwsJGnqjBExot3mjvos0SlAqPQlw5AoC1xkpyeSmAAU4DnjTGvA5uBJ6y1TwK3A5cYY3YDp6lGuAAAIABJREFUlzi3Af4C7AP2AL8GPg/gNO/8HvCq8+e7kYaeE01Hdy8HmztiNoEkQqNURUREZCSstWzcWcd55YVkpCa7FseVi6aRnZbMg69WH/e89q4e/uu5fZx/ShGnl+bGKbqxiUxI0TYSkfHD3xQCoCR37B9QF0QSGAk0icRTTTyttfuARUMcDwArhjhugS8c41r3AvdGO8ZEU90Y2wkkERqlKiIiIiPxVu1hDjZ38OVLTnE1juz0FK5cNI1Ht9XyrStOJScjdcjzfv9KNYG2Lr7o4ckjRyrJzSQzNVmNPEXGkZqgk8CIYg+MhgRKYHitAkOiLJJQiHUFxuSsVPKyUtmvSSQiIiIyDBt21GEMXDQvvuNTh3Ld0lJC3b08/vrBIe/v6O7lV8/u472zC1hSlh/n6EYvKckwuzib3XUtbociIlHidxIY03IzxnytnPQU0lKSCLQmzhYST1VgSPRFRqjGOoEB4W0kVUpgiIiH/PvVC9wOQUSOYcPOQ5xemtu/B9tNp5fmMu+kHB58tZq/P3vGUfc/sLma+pZO7rp+sQvRjU15cQ4v7wu4HYZIwvLaWsLf1E5xTjrpKWPfemeMoTA7jYYESmCoAmOcqwy0kZuVyuSsocsho0mjVEXEa2YX+Zhd5HM7DBE5wqHDHbxR0+za9JEjGWO4bmkpr9c0s7328KD7Ont6ufuZfZxVls+ykxOn+iJiTrGPg80dtHR0ux2KSELy2lqiJhidCSQRBb50Am3aQiIeURVo72+wGWtlBdkapSoinrJ++yHWbz/kdhgicoQNO+oAPJPAAPjQ4hLSUpKOaub50JYa3jncwS0r5mCMcSm60St3GnnurVeVrMhoeG0t4W8KMT0vev0NC3xp6oEh3lEZaKMsxg08I8oKszRKVUQ85dfP7ePXz+1zOwwROcKGHYeYnpfJKVO886lmblYa73/PSTxS4e//MKa7t49fbtrL4hm5nDun0OUIR6d/Eskh9cEQGQ0vrSX6+iy1TSFKcqNXgVHoS0+oHhhKYIxjnT291DaF4lqBAbBfk0hERETkGEJdvTy/p4GL50/xXEXDdUtLOdzRw//+LdzM85HX/PibQnzxonLPxTpcM/KzSEtOYo9GqYokvLqWTrp7bZS3kKQRaO0iPODT+5TAGMdqgiH6LPGrwHASGJHGoSIiIiJHemFPA509fayY7/70kSMtm1XAzIIsHth8gJ7ePv7z6T0sKJnMBXOL3A5t1FKSkzi5KJvdSmCIJDx/U/h91vQoJjAKs9Pp6u3jcEdP1K4ZS0pgjGPVTiIhXhUYGqUqIiIiJ7J+xyF86SmcPavA7VCOkpRkWFAyiVf2NzLnX/+X6sZ2zirLS9jqi4g5xT5VYIiMAzXOCNXp0dxCkpMGQCBB+mAogTGOVTqJhHhVYACUFWqUqoiIiAytr8+yYWcd7zuliLQU7y1D11b4Wb+9btCx32+uZm2F36WIoqO8OIcDwXZCXWq0LpLIIgmMqG4hyQ6Psg60JUYfjBS3A5DYqQq0k5OeQn52Wtyes6wgm837G+P2fCIix/Pj6053OwQRGeBNfzP1LZ2e3D4CcOe6XXT09A06Furu4851u1i1uMSlqMaufIoPa2FvfSvvKZnsdjgiCcVLawl/U4j87DSy0qL3Nr7AF36v2NCiCgxxWWWgjRkFWXEte9QoVRHxkmm5mUyLYpmliIzNhh2HSDJw4VxvJjBqm0IjOp4oIpNItI1EZOS8tJaoCUZ3AglAkS9cgdGQIBUYSmCMY1WB9v7GmvGiUaoi4iWPv17L46/Xuh2GiDjW76jjzJl55MWxOnQkjvUmxStvXkarrCCb5CTD7jqNUhUZKS+tJfzB9qgnMCI/j9UDQ1zV09vHgcZ2Zsax/wVolKqIeMvvXq7idy9XuR2GiBCuYth+8DAXz5/idijHdOvKuWSmJg86lpmazK0r57oUUXSkpSRRVpDF7kOqwBAZKa+sJay1+JtCUZ1AApCanERuViqB1sSowFAPjHGqtqmDnj4b/woMjVIVERGRIWzYcQiAFR5OYET6XNy5bhe1TSGm5WZy68q5Cd3/IqK8OIe3PV6BsbbCPy7/7UWiIdDWRUd3X1QbeEYUZKfRkCAVGEpgjFORCSTxrsDQKFUREREZyvoddZQVZDG7KL4frozUqsUl4/JNc/kUH0/tOERnTy/pKcknfkCcra3wc9uaNwk5fdT8TSFuW/MmwLj87yEyUv7ICNW86L+/K/SlJ0wFhraQjFORUaZlhfFfJJQVZlOpLSQiIiLiaOvs4aW9AVbMnxLX5uLyrjnFPnr7LJUN3qySvXPdrv7kRUSou5c71+1yKSIRb/E7zYSj3QMDwgmMhrbEqMBQAmOcqgy0k5GaRHFOetyfu6wgW1tIREREpN9zuxvo6u3z7PjUiSAyicSrjTzH6wQYkWipCYbfX8VkC4kvLWHGqGoLyThVFWijrCDblU85ygqyWbvNT0d3Lxmp3itRFJGJ45cfO9PtEESEcP+LSRkpLC3LdzuUCWt2kQ9j8Gwjz2m5mf2fMB95XMRNXllL+IMhcjJSmJyZGvVrF/rSOdzRQ1dPH2kp3q5x8HZ0MmqVgfhPIInQKFUR8Yr87DTyPTquUWSi6O2zbNxZxwVzi0lN1tLTLRmpyczIz2JPvTcTGLeunEtK0uAP3lKTTcJPgJHE55W1hL8pFJPtIxCuwABobPN+Hwz9FhmHevss1YH2uE8gidAoVRHxioe2HOChLQfcDkNkQtt2oIlAW5e2j3hAebGPPR6twFi1uIRTp+aQnGQwQHpKElg4c2ae26HJBOeVtURNMPojVCMKssNtBxJhEklMEhjGmGuNMTnO1980xqwxxpwRi+eSo71zuIOu3j5mupzAqNQkEhFx2cNba3h4a43bYcgoaC0xfmzYcYjkJMMFpyiB4bbZxT72NbTS09vndihHsdbyzuFOrlg4lf23X87Gr1xAWkoSt615E2ut2+HJBOaFtYS1Fn8wFJMJJABFOeEKjAmbwAC+Za1tMcacC6wE7gN+GaPnkiP0TyBxaQtJZJRqpRp5iojI6GktMU5s2FHH0rI8JmdFf9+2jEx5cQ7dvZYqD27zPdjcQV1LJ4tLc4HwpIWvf2A+z+9p4I8e+PRbxE2HQz20dPbEbguJU4GRCKNUY5XAiMxAuhz4pbX2UcD9jUMTRGQCyEwXRqhGaJSqiIiMkdYS48CBxnZ2HWrh4vlT3A5FCG8hAW828tx2oAmAxTPe3TLy0bNmcPasfL7/xA7eae5wKzQR19U0xW4CCbzbAyOQAKNUY5XA8BtjfgV8BPiLMSY9hs8lR6gMtJGWksTUSRmuxTBLo1RFRGRstJYYBzbsOATACiUwPGG2k8DY48FRqhXVQdJSkpg/dVL/saQkwx0fXkh3bx/fXKutJDJx+YPhCT2x6oHhS08hLSWJhglcgfERYB1wmbW2CcgHbo3Rc8kRqhraKc3LJCkp/iNUI2YWZFPbHKKju/fEJ4uIiBxNa4lxYP2OOmYXZTPLxapQeZcvPYWS3Ez21HmvAqOiuon3TJt01AjHssJs/uWSuazfUcdjr9e6FJ2Iu2qcBEastpAYYyjypSdED4yUWFzUWttujKkDzgV2Az3O3xIHlYE21yaQRERGqVY3tnPKlBxXYxGRies3nzrL7RBklLSWSGxrK/zc8eRODjZ34EtPYW2Fn1WLS9wOSwhXYez2WAKju7ePN/3NfGzZzCHv//S5s/jzmwf5zuPbOXdOIQW+9DhHKBOZF9YS/qYQGalJMR3nWuBLm7g9MIwx3wa+BtzmHEoFfheL55LBrLVUBdpdm0ASEfmkRX0wRMRNmWnJZKYlux2GjMJY1xLGmGRjTIUx5s/O7VnGmFeMMbuNMQ8aY9Kc4+nO7T3O/WUDrnGbc3yXMWZltL638W5thZ/b1rzJQadnQWtnD7eteZO1FX6XIxNwRqnWtdLb553tGDsPttDZ08fiGblD3p+cZLjzmoW0dHTzb49vj3N0MtF5YS0RmUBiTOwq7Auy0xKiAiNWW0g+BHwQaAOw1tYC+hg+DupbOgl191JW6M4EkoiZGqUqIh7w25cq+e1LlS5HIaM01rXEl4AdA27fAfzYWlsOBIHPOMc/AwSttXOAHzvnYYw5FbgeOA24DPiFMUbZsGG4c90uQkdsIQ1193Lnul0uRSQDlRf76Ozp699T7wUVB4LA4AaeRzplSg63XFTO46/X8te33olXaCKeWEvUNLXHbPtIRKEvfeJWYABdNtxlxwIYY7TxMU4io0vdrsCYnJlKfnaaRqmKiKv+/MZB/vzGQbfDkNEZ9VrCGDOd8PSS/3JuG+Ai4GHnlPuAVc7XVzm3ce5f4Zx/FfCAtbbTWrsf2AO4X0fscQ2tnfibhn5jXHuM4xJf5VOcSSQeauRZUd1EUU460yYfvwH9zRfMZt5JOXxz7d9oDnXHKTqZ6LywlghXYMQ2gVHgSyfQ1un5ZrmxSmD80ekcnmuMuRFYD/w6Rs8lA0QqHsoK3K3AAJhZkKUtJCIiMlpjWUv8BPgq0OfcLgCarLU9zu0aINKQoQQ4AODc3+yc3398iMfIERrburj9f3dy3h1PH/OcaTH+9FCGZ05RuJDJS408tx1oYnFp7gnL41OTk7jzmkUE2rr4wRPaSiITQ1tnD8H27piNUI0o9KXR3Ws53NFz4pNdFKsmnv/XGHMJcBiYC/wfa+1TsXguGawq0EZKkol5idFwzCrI5uV9AbfDEBGRBDTatYQx5gqgzlq71RhzQeTwUE9xgvuO95iBz3cTcBPAjBkzThTeuNPc3s1/Pb+Pe5/fT3t3L1ctmsap0ybx46d2D9pGkpmazK0r57oYqURMzkqlOCfdM408g21d7G9o4yNLSod1/oLpk7np/JP55aa9XLloGueVF8U4QhF3Rara4rGFBMKVdJMzU2P6XGMRkwQGgLPIUNIizioD7UzPyyQlOVbFNcNXVpjNmgo/Hd29ZKRq27CIiIzMKNcSy4EPGmM+AGQAkwhXZOQaY1KcKovpQGQeYw1QCtQYY1KAyUDjgOMRAx8zMMbVwGqAJUuWeLvuNopaOrq59/lK/uv5fbR09HD5gqn808XllDuTx4pzMrhz3S5qm0JMy83k1pVzNYXEQ+Z4aBLJtgNNAMds4DmUL60oZ91b7/D1P73JX798PtnpMXtLI+K6SL+a6XmxrbAv8IUnnARau5jt4bxgVP9vN8a0MMSnE4Q/xbDW2knRfD45WlWgzfX+FxEznW0sGqUqIiLDNda1hLX2NpzJJU4FxlestR81xjwEXAM8ANwAPOo85DHn9kvO/RuttdYY8xjwe2PMj4BpQDmweYzfXsJr6+zhNy9W8uvn9tHU3s2lp07hny4+hVOnDf7PsmpxiRIWHlZe7OPhrTVYa2M61WA4KqqDJBlYUDJ52I/JSE3mPz68kGt/9RL/8eROvnPVe2IYoYi7aoLhnoIx74GRHa7ACHh8EklUExjWWr1LdZG1lqqGds48TgfneIqMUt3f0KYEhoi44sF/OMftEGSEYriW+BrwgDHm+0AFcI9z/B7gt8aYPYQrL6534njLGPNHYDvQA3zBWtt79GUnhlBXL799uZK7n9lHY1sXF80r5ssXn8KC6cN/0yneMWdKDm1dvRxs7nC9N0nFgSbmnjRpxFUUS8ryueGcMn7zYiVXLJrG0rL8GEUoE53ba4maphBpyUkUOVs8YqXQqcDw+ijVmNZbGWOKCZdvAmCtrY7l8010jW1dtHT2eKgCIxxHlUapiojIKI1lLWGt3QRscr7exxBTRKy1HcC1x3j8D4AfjCjgcaaju5c/bK7mF5v2Ut/SyXnlhXz5klM4wyMflsjolBeHJ5HsqWt1NYHR12fZdqCJKxdNG9Xjb105l/U7DvG1h9/gL186T1uWZVzyB0NMy80gKSm21VL52ZEEhrdHqcakUYIx5oPGmN3AfuAZoBL431g8l7yrqjFcXlRW6P4EEnh3lOr+Bo1SFRF3rH52L6uf3et2GDIKWku4q6unj9++XMUFd27iO49vZ3ZRNn/8h3P47WfOVvJiHIgkMNzug7GvoZWWjh4Wlw6//8VA2ekp3H71QvY1tPGT9bujHJ1ImNtriZpgKOYTSABSkpPIy0ol0ObtCoxYdXr8HrAMeNtaOwtYAbwQo+cSR6TSwSsVGBAe56oKDBFxy4YddWzYUed2GDI6Wku4oLu3jwc2V3Ph/93Et9b+jZK8TH7/2bN54KZzOGuWSvTHiwJfOnlZqeypa3E1jteqR97A80jnlhdy3ZJSVj+7lzdqmqIVmkg/t9cS/qZQ3CZMFvjSaWjxdgVGrLaQdFtrA8aYJGNMkrX2aWPMHTF6LnFUNrSTZGLf4GUkyjRKVURERkdriRhaW+EfNCXkXy4ppw/DXRt2U93YzqLSXP796gWcX17oepNHiY3y4hx2H3K3AmPbgSZyMlI4udA3put84/L5bHq7jq8+/AaP/eO5pKW4P41PJBo6unupb+mM+QSSiEJfmucrMGKVwGgyxviAZ4H7jTF1hBtgSQxVBdqYOjmT9BTv7P/TKFURERklrSViZG2Fn9vWvEmoO9yT1N8U4l8eegMLnDZtEvfcsISL5hUrcTHOzZni44k3Dro6iaSiuonTS3PHvLd/cmYq31+1gBv/Zwu/3LSXL11cHqUIRdxV2xQeoRrPCowdtYfj8lyjFav05FVAO/Bl4ElgL3DlcB5ojEk2xlQYY/7s3J5ljHnFGLPbGPOgMSbNOZ7u3N7j3F824Bq3Ocd3GWNWRvl786zKQLtn+l9EDBylKiIiMgKjXkvI8d25bld/8iLCEm7g9udbzmXF/ClKXkwA5cU+mkPd1Ls0caCts4dd7xxmcZR6qlxy6hQ+uGga//n0bna94+7WGJFo8UcSGHGqsC/MTvP8FJKY1ldZa3sIz1WvBIabyvkSsGPA7TuAH1try4Eg8Bnn+GeAoLV2DvBj5zyMMacSHoF2GnAZ8AtjzIT46L8q0Oap/hcweJSqiEi8ZaQmq/orwY1yLSHHEflE70jBti4lLiaQ8uLwxOI9LjXyfKOmmT7LqBt4DuXbV55KTkYqX334dXp6+6J2XZnY3FxL+IPhn9fxahFQ6EvncEcPnT3enRoeqwTGs0CGMaYE2AB8CvjNiR5kjJkOXA78l3PbABcBDzun3Aescr6+yrmNc/8K5/yrgAestZ3W2v3AHoYYmzbeNLd3E2zvpqzAaxUYGqUqIu6579Nncd+nx/2vgPFqVGsJObFjjc10c5ymxN+cAaNU3bDtQLjh5ulRTGAU+NL5tw+exus1zdz7wv6oXVcmNjfXEjXBEMlJhpMmZZz45Cgo8KUD0Njm3UaesUpgGGttO3A18DNr7YeAU4fxuJ8AXwUiKdMCoMn59AWgBihxvi4BDkD/pzPNzvn9x4d4zLhV1ei9CSSgUaoiIjJqo11LyAl84pwZRx3LTE3m1pVzXYhG3DJlUjo56SmuNfKsqA4yqzCbvOy0qF73yoVTuXj+FP7fX99WBbAkPH9TiJMmZZCSHJ/GtAW+8P+PgdYJmMAwxpwDfBR4wjl23IahxpgrgDpr7daBh4c41Z7gvuM95sjnvMkYs8UYs6W+vv544XleZSCcICjzWAIDwqNUK/ULRERccNeG3dy1YbfbYcjojHgtISfW22dZ99YhMlOTOGlSBoZwc7gfXr2AVYvH/ec9MoAxhjlTfOx2YZSqtZaKA01R3T4SYYzhBx96D2kpSXztT2/Q1zfk2wCRYXNzLeEPhuLW/wLCU0gA13rjDEesFgJfAm4DHrHWvmWMORl4+gSPWQ580BjzASADmES4IiPXGJPiVFlMB2qd82uAUqDGGJMCTAYaBxyPGPiYQay1q4HVAEuWLEnon25VToJgRr63tpCARqmKiHte2NMAwBdXqCN9AhrNWkJO4L4XK3mtuokffWQRV58x3e1wxGXlxT427qyL+/P6m0LUt3Ry+ozoJzAApkzK4FuXn8pX//QG92+u5uPLZsbkeWRicHMtURNsZ9nJBXF7vkJnC8mEq8Cw1j5rrf2gtfYO5/Y+a+0XT/CY26y10621ZYSbcG601n6U8GLlGue0G4BHna8fc27j3L/RWmud49c7U0pmAeXA5ih+e55UGWjnpEkZZKZ5r1ldWWE2tc0ddHR7txmMiIh4y2jWEnJ81YF27ly3iwvnFvEhVVsI4UaeDa1dBOO83z3S/2JxaXQmkAzl2iXTOa+8kNv/soOaoLYyS+Lp7u3jncMdcWvgCe/2wAh4uAIjPptpxuZrwD8bY/YQ7nFxj3P8HqDAOf7PwNcBrLVvAX8EthMeu/YFa+24f+ccnkDiveoLCCcwQKNURURE3GKt5etr3iA5yfCDDy3QtBEBYM4Up5FnfXz7YFRUN5GeksS8qTkxew5jDP/+oQVY4BuP/I3w55wiieOd5g76bPxGqAJkpyWTnpLk6VGqnkxgWGs3WWuvcL7eZ609y1o7x1p7rbW20zne4dye49y/b8Djf2CtnW2tnWut/V+3vo94qgy0e7L/BdA/GUWNlERERNzxwKsHeHFvgNs+ME/TRqTfnKJwAiPejTwrqoMsnD6Z1Bg3JizNz+Jrl83j2bfr+dNr/pg+l0i01TgjVEty4/chtTGGQl/6xNtCYoxZPpxjEh1tnT00tHYys9CbFRiRyShq5Cki8ZaXlUZeVnQ73EesrfCz/PaNzPr6Eyy/fSNrK7Q4jiatJaLnYHOIf39iB8tOzufvlh49gUQmrpLcTDJTk+PayLOrp4+/1R6O6vjU4/n4spksLcvju4+/Rd3hjrg8p4wvsVxLHI+/KZzAiOcWEgg38mzw8BjVWDXx/BlwxjCOSRRUeXgCCbw7SjUyKUVEJF7u/viZMbnu2go/t615k5DT28ffFOK2NW8CaJJD9GgtEQXWWr75yN/o7uvjjg8vJClJW0fkXUlJhjnFPvbUxa8CY8fBw3T19LF4Ruz6XwyUlGS4/cMLef9Pn+Mz971KY1sXtU0dTMvN5NaVc/UzW04oVmuJE4n0bpmamxHX5y3wpXPIw8m+qCYwnHFn7wWKjDH/POCuSYD3ukuOE1WBcGWDV3tggEapisj4cue6Xf3Ji4hQdy93rtulxfAYaS0RXY9uq2XDzjq+efn8/opIkYHKi328FMdpcRXVQQAWx2gCyVBmF/m49NQp/PmNg/3HlHgWr/MHQxTnpJOeEt9ffYW+NN6qbY7rc45EtLeQpAE+womRnAF/DvPuJBGJskhlg5cXJmWF2f2JFhGReLnjyZ3c8eTOqF+31inrHO5xGRGtJaKkobWT7zz+Fotn5PKp5bPcDkc8as4UHwebO2jp6I7L81UcaOKk/8/encdHVZ+LH/+cmWSyTUjIDklYEwKEBAIIIogVlbhgRRRba63Wqr2/2tpq3bC1rXotVrvde3u7XEWt1rqgiIoggrixyBpI2EkIJBmykGWyTpaZOb8/JgMBQsgyM+fM5Hm/XnlBZibnfIFh5jvPeZYhoQyL8m1a/K7OwElX7sCzED3x1l7iQixWm8/LR8CVgVHT1K7bxrcezcBQVfUL4AtFUV5RVfW4J48tzu94TTNxZhPmEG9VBA3cqNgIVuyy0NrhIDRYLqAJIXxj1/FzN6yeEB0eTF3LuZt9aY44cLKX8Jxff7CP5jYHz92UjVFKR8R5uBt5FlY1+aSsI6/E6rP+F12VW7tPiZfAs7gQb+0lLqSszsZkDf6vxEaYsDtVGmx2osKDfX7+C/FoBoaiKH/u/O1fFEX54OwvT55LnHasplnX2RdwepTqcemDIYTwc9aWdtrtTs6eQmkyGng4N0ObRQUQ2Ut4xsd7K/gov5z7r0gjPdF7oyqF/3M/P474oA9GTVMbJbUtPi0fcTtfgFkCz0KPnE6V8nobyRo8P+PMIQCc1OkoVU9fsn+t89ffe/i4ogfHa1qYNTZW62X0aLR7EklNMxlJspESQvivpasP0mp38tD8DP69tYQTVhtGg0KYycDl4xO0Xl4gkL3EANW3dPDE+3uZOGwIP7xsrNbLETqXOjQMU5DBJ408d5daAXzWwLOrh3Mzzmi+DBAWbJTAs9ClqsY2OhyqJiUk7gBGTVMbaQlmn5//QjxdQrKz89cvPHlccX6tHQ7K61t1O4HEzT3iVRp5CiH82ZaiGt7aUcoPLxvDfZencd/laQDsKbWy6G+befKDffzxW1M0XqV/k73EwD390X5qm9t5+c6LCDZ6ut2ZCDRBRgNj4iI4Uun9Uap5JVaMBoWs5Civn+ts7kadv1hZQHObg+HRoTySO14aeApdck8gSdakB4ZrZGyNTkepeuVdTVGU2YqirFMU5bCiKEcVRSlWFOWoN8412JXUuht46ncCCcCQ0GBiZZSqEMLHhkWFMizKM+PHWjsc/OK9AlJjwvjZFePOuG9yajQ/vjyNFXkW1hSUn+cIoi9kL9E/Xxw+yTs7y/jh3DFM0uBDovBP6YmRFJ70fgZGXmkd45MiCTNp0w9tYU4y/7lwEgAv3XmRBC9Er3hyL9Fbls7eLKkaBjCqB0kJidsy4AFgJ+C4wGPFALgzGvSegQGuIItkYAghfOnP387x2LH++nkRR6ubefWuGd1uvn88L40NB6t4/L0Cpo0aSkKkbzc7AUj2En3U1Gbn8RUFjI2P4P4r0rVejvAjafFmVuWfoKXdTrjJOx8PHE6VPaX1LMwZ7pXj91Z2iqv/Rn5pPeOThmi6FuEfPLmX6K2yOlcAQ4seLTHhJhQFqpsGUQYGUK+q6hpVVatUVa1xf3npXIOauymmPwQwRsVFcExGqQoh/NCRykb+9nkhC6cMZ+64+G4fE2w08KdvTaal3cGSdwt0O37Mj8heoo+e+/ggJ+ptPHfzZJn4JfokPdGMqsLRk97bpxWdbKKpzU5Oqu/7X3Q1OjaCyJAg8i1WTdchRE/K6myOQe7JAAAgAElEQVTERJi8FlDsSZDRwNBwEzU6zcDwVgDjM0VRnlcUZZaiKFPdX14616B2rKaZ6PBgXY64Odvo2AjK61tp7ZALaUII33jyw308+eG+AR3D6VRZsqKAiJAgfrlgYo+PTUuI5NGrx/PpwSre3lE6oPMK2Uv0xdajNby65Th3XjKKaSO1/YAo/E96Z6O+I1Xe64ORV+IaRanFBJKuDAaFrJQo8svqNV2H8B+e2Ev0lcVq06SBp1tshIkanWZgeCukM7Pz1+ldblOBeV4636B1vKZF9yNU3UZ2GaUqk0iEEL6w/0TDgI/x5vZSdhyv47mbs0915u7JnZeMYv2BSp76cD+zxsQxQuc9inRM9hK91Nrh4LEVrv4sMlFB9MfI2AiCDApHKr3XByOvxEpUWDCj47Tft2anRLNs41Ha7A5CgiRbSfTME3uJviqrayFDwxHYceYQ3fbA8EoGhqqql3fzJRsOLzhW08woP9kcdx2lKoQQ/qCqoZWlaw5w8ZgYFk9L6dXPGAwKzy+ejEFReGj5HhxOKSXpD9lL9N6f1h2muLqZZxdla5JuLPyfKcjAqLgIr45S3V1qZUpqNIqieO0cvZWdEkWHQ+VgufcnrwjRV6qqcsJqI1mD/hdusWaTbqeQeOVdTlGUX3V3u6qqT3njfINVu93JCauNRVN7t6nWmoxSFUL4myc/3E+b3clvb8zq06Y7OTqM33wzk58v38OyjUe5d+5YL64yMMleonf2lFp54auj3DojldlpcVovR/ix9AQzhyq884G+qc3OocpGrp6U5JXj91V2imtCT36Zlcmp2pa0CHG2muZ2WjucmpaQDLoMDKC5y5cDuAYY5aVzDVpldS04VfwmA+P0KFUJYAgh9O/TA5V8VFDOTy5PY0y8uc8/v2hqMrmZifx+7WEOVvg+/TQAyF7iAtrtTh55J5+EyFCWXDtB6+UIP5eWYOZYTTNtds/3KssvtaKqkDNCH/1ZkqPDiI0wsUf6YAgdck8gSR6q3We82AgTja12XfYu9EoGhqqqf+j6vaIovwc+8Ma5BjP3BBJ/6YEBnZNIqlu0XoYQYpAYE9+/18fmNjtPrNxLeoKZH17Wv+wJRVH47Y1Z5P75Sx54aw/v3zcbU5C3rhsEHtlLXNj/flbIocpGlt0xnSGh+m/mLfQtLcGMU4Xi6maPjxfNK3VN/JiSoo9sB0VRyE6JokACGKIX+ruX6C+LO4ChYQlJXKSr51dtc7smo1x74qudVDgwxkfnGjTcmQwj/SQDA1xrlQwMIYSvLF2UzdJF2X3+uT98cpgT9a08e1PWgIIOseYQli7K5kB5A//16eF+H0cAspc4w8GKBv73M9do3ysmJGq9HBEA0hNcDQO90cgzr8TKmPgIXU3Ny06J5khVIy3tdq2XInSuv3uJ/rJYXRd7kzWeQgLochKJVwIYiqIUKIqS3/m1DzgE/Jc3zjWYHa9pwRwSdOoJ5g9klKoQQu/yy6y8srmY7148gmkjYwZ8vKsmJnLL9BT+9nkRO4/XemCFg4PsJc7P7nCVjkSFBfOr6zO1Xo4IEGPiIzAocMTDjTxVVWV3aR05qfooH3HLTonCqcJei5T4CX0pq7MRGRpEVJh2Ab/YzqlreuyD4a1W1Qu6/N4OVKqqKuFNDztW08zI2HBddHPuLRmlKoTwpSUr8gF6feXE7nDy2LsFxJlDeOTq8R5bxxMLJrK5qIYH397D6vsvJSJEJkX0guwlzmPZxmLyy+r5y3dyiPGjixhC30KDjYyICafIwwGMsjob1U3t5IzQR/mIW3ZnOUt+mZUZowcerBaBq697iYGy1Gk7gQQgXscBDG+NUT3e5csiGw7vOF7Twig/6n8Bp0epFsskEiGEDxw92czRk71/vXlpUzH7yxt48puZHu0pEBkazO8XT6aktoXfrj7gseMGMtlLdO/oySb+uO4wuZmJXJc1TOvliACTlmDmSJVnJ5Gc6n+hs2kf8ZEhDI8KlUae4oL6upcYKIvVRoqGDTzBNUYV0OUoVekm5qfsDieltS1+1f8CTo9SPS59MIQQOlNa28Kf1h3hygmJXhn1d/GYWO6eM5rXt5bw2aEqjx9fBD6nU+XRd/MJCTLw9A2T/CoDU/iHtIRIiqub6XA4PXbMvJI6QoMNjNdh5m12SjQFZVatlyHEKaqqUlZn03SEKkC4yUhosIGawZKBIbzvhLUVu1P1uwyMDQeqMCiwdM1BZj+7gZV5Fq2XJIQQqKrKL1fuxaDAUzdkeu2D4c/nZzAu0cyj7+RjbdHfVQ2hb//aepztx+p4YsFEEoaEar0cEYDSE8x0ONRTk+48Ia/ESnZKNEFG/X3syE6N4lhNC/UtHVovRQgAGmx2mtrsmpeQKIpCnDmE6sHSxFN4nz9OIFmZZ2HJigKcqut7i9XGkhUFEsQQQmjugz0n+OLwSR7KzfDquLDQYCN/vGUKtc3t/HLlXq+dRwSe0toWnl1zkLnj4rl5WorWyxEBKj3RDEChh8pI2uwO9p9o0F3/C7fJ7j4YFsnCEPpQWucKHmqdgQGuRp6DpgeG8D53CcaoOP/JwHh+7SFsZ00fsXU4eH7tIY1WJIQIdBOHD2Hi8CE9Psba0s7Tq/YzOSWK780a5fU1TUqO4mdXprMqv5wP9pzw+vmE/1NVlcffK0ABfnujlI4I7xkb7w5geKaR5/4TDbQ7nOTorP+F26TkKADypQ+G6EFv9hKeYrHaAG1HqLrFRZh0OUZV2qD7oZV5Fp79+CAAi/66iYdzx7MwJ1njVV3Yic7/kL29XQghBurXvRgxuXT1QepaOnj1rpkYDb75YPgfl43l04NVPLFyLzNGxZAUJeUA4lwr8yw8v/bQqQ3tzVOTNW/sJgJbREgQydFhHhulmlfiymzIGaGvEapuUWHBjI6LYE+pZGCI8+vNXsJTLHWu13s9vNbHmk0UWPQX3JMMDD/jLsNobnNlMlisrX5ThnG+tGxvpmsLIURPvj5aw1s7Srn70tE+u7oCEGQ08MdbptBud/LIu/moquqzcwv/4H6/t3QJ8n9UUO4X7/fCv6UlmDlS6aEARqmV4VGhJOq4Z0t2SpRkYAjdKKuzERZsZGi45yah9VecOYTa5nacTn3tUSSA4Wf8uQzj4dwMwoKNZ9wWFmzk4dwMjVYkhAh0P3szj5+9mdftfa0dDh5/r4DUmDB+dsU4H68MRsdF8Ph1E/jy8En+tbXE5+cX+tb9+73TL97vhX9LTzBTdLIJhwc+tOSV1Ok2+8ItOyWaioZWqhpatV6K0Kme9hKeZrG2kDw0TBelgrHmEOxOlYZWfTW5lQCGn/HnMoyFOcksXZR1Rlfdn88f5xflL0II/1Re30p5ffeb0r9+XsTRk808szCLMJOx28d423dnjmDuuHh++9EBiqtlvLQ4zZ/f74V/S08002Z3UlY3sEkkJxvbKKuzMUWn/S/cJqdIHwzRs572Ep5msWo/QtUtzmwC0F0jTwlg+Bl/L8NYmJPMpsfm8dUjlwPglLRpIYQGjlQ28rfPC1k4ZThzx8Vrtg5FUXjupmxMQQYefHs3dodTs7UIffH393vhv9ISIoGBN/LcXeruf6HvAMbE4UMwKJBfJn0whPbK6myaj1B1izOHAOhulKoEMPzM/fPSzrnNH8swUmPCmThsCGv3VWq9FCHEION0uiY6RIQE8csFE7VeDklRoTy9cBJ5JVb+8eVRrZcjdELKLoVW0hJck0gG2sgzr6SOIINyatKHXoWbghiXGMkeycAQGmtus2Nt6dDFBBJwNfEEdDeJRAIY/qazHCrObEIBkqPDWLooyy/LMHIzk9hVUkdVo9QcCtFfK/MszH52A6Mf+4jZz26QBn+98Ob2UrYfq+MX1044dXVBa9+cPJwF2cP407rD7NVhx2/he13LLv39/V74l6iwYBIiQwbcyDOvxMrE4UMIDdamRK8vXI08rdJQWWjK3bRZDxNIAGIjXHukmmZ9lZDIGFU/8+9tpaQnmPnkgbm6aO4yELmTEvnT+sOs21/JbTNHar0cIfyOe0qBu9GfxWpjyYoCAPmQ02nqyDObx1U1tLJ0zQFmjYnl5mkpGq2qe/+5cBLbimt58O3dfPDjOX6x6RfetTAnWf4vC02kJ5oprGrs9887nCr5ZVZu0tnr7Plkp0Tz9o4yyupspMbo48Oj0I+z9xLe4u47o5cSkpgIE4oC1Y36CmBIBoYf2Xeinj2lVm6dMcLvgxcAGYmRjIwN5xMpIxGiX/x5KpGvPHr1eB69evyp759ctZ82u5Nnbpyku9fR6HATz92czeHKJv647rDWyxFCDGLpCZEcqWrqd0bCkapGmtsduu9/4TY5xbVOaeQpunP2XsJbLHXuDAwPBzDKy+Gyy6Ciok8/ZjQoxISbqG6WEhLRT29uK8UUZGDR1MC4GqMoCrmZSWwuqtbdeJ7uSKq+0BuZUtA3nx6o5KP8cu6fl8aYeLPWy+nWNzISuG3mCP7vy6NM/8918nojhNBEWoKZlnYHJ/o5eSGvpLOBZ6q+R6i6ZSRFYjIapJGn0FSZ1YbJaCDe0+WtTz8NGze6fu2jWLOJGplCIvqjpd3OyjwL12UNIzrcpPVyPGb+xEQ6HCqfHazSeik9cqfqW6w2VE6n6suHCqElmVJwYf/x2k7+47WdNLfZ+dX7+xiXaObeuWO1XlaPspOjUHB1/ZbXGyGEFtI7G3n2dxJJXkkdQ8ODGRnrH+UYpiADE4ZFskcCGKIb7r2Et5XV2RgeHYrB4MEM0fJyePllcDpdv/YxCyM2IkSmkIj+WZVfTmObnVtnjNB6KR41dcRQ4swhui8jkVR9oUcP52YQbDzzTS7IoMiUgi7qWtqpa2nnj+sOY7HaWLooC1OQvt/6/ntDIWcnbcvrjRDCl05NIqnsXx+M3aVWpqRG665UryfZKdHstTTgdEojT3Em917C2yx1Ns9PIHnqKWjvXLvD0ecsjLjIEMnAEP3zxrYSxsZHcNEo/0jF6y2DQeGqiYl8fqiK1rMCBHoiqfpCjxbmJDMu0YzRoKAA4SYjdqeqm/FbWluZZyGvxMrW4lqWbSxm9tgYpo2M0XpZFySvN0IIrcWaQ4iJMPUrA6OhtYMjVU3kjPCvPWt2ShRNbXaOVg9s+ooQ/VVWZyMl2oNZS+Xl8MorruwLcAUy+piFERthkjGqou8OlDeQVxI4zTvPlpuZSHO7g02F1Vov5bwkVV/okd3hpKTGxi3TUyl+9jq2/+JKRsSE8+Dbu2lqs2u9PE25y77aHc5Tt+0ssfpFGYa83ggh9CAtwcyRfgQw8kvrUVX8poGn2+RUaeQptNPa4aC6qc2zF6Gefvp08MKtj1kYcWYTjW12XV1olgCGH3hjWwmmIAM3TfWPUVR9dcnYOCJDgnRdRvJwbgYhZ6WdKwo8dNU4jVYkBORb6mlsszM7LRaAiJAg/njLZMrqbDzz0X6NV6et7sq+WjucflGG8XBuBmHdjFB1/zsLIYQvpCeYOVLZ2OdJJHkldSjK6YCAvxgbbybcZJQAhtCEO8vSoxNItmw5XT7i1t4Omzf3+hBxnQ1Fa3Q0iURXAQxFUUIVRdmmKMoeRVH2KYryZOftoxVF2aooyhFFUd5SFMXUeXtI5/eFnfeP6nKsJZ23H1IUJVebP9HA2dodvLfLwrWTkhgaETjNO7syBRm4fHwC6w9U4tBp3eHCnGSmjnS9ESvA0PBgVBWa2gf3VW6hrS1FNQDMGnP6g+30UTH8cO5Y3thWyoaD+g0Keps/l2EszElm6aIskqPDUIDhUaFMSIpk+c4y3t+t/wwSIURgSE8w09Bq52Qf6993l1oZG29mSGiwl1bmHUaDwqThUdLIU5xjdlocs9PivHqOss4RqsmezLbMywNVdWVhpKbCjTe6vs/L6/UhYt0BDB31wdBVAANoA+apqjoZmAJcrSjKxcDvgD+pqpoO1AE/6Hz8D4A6VVXTgD91Pg5FUSYC3wYygauBvyqKcu7lLD+wKv9EQDbvPFtuZhI1ze3sOFar9VK61drhYK+lgRtzkil+9jp2PXEVc9LieO7jQ1T0c8SYEAO1qbCa8UmRp95c3B64Kp3xSZE88k4BtTqKmPuSv5dhLMxJZtNj8yh+9jo2L7mC9+6bzYxRMTz49h7W7R+8ganekIshQnhGWkIkAIWVvS8jUVWVvFIrOX6WfeGWnRLF/hMNdDicF36wGDTuvyKd+69I9+o5LJ0XWLzSx0xR4PbbYdiwPv9orNl1AV1PfTB0FcBQXdyvksGdXyowD3in8/Z/Ags7f39D5/d03n+F4moScQPwpqqqbaqqFgOFwAwf/BE87o1tJYyJj2DGaP03nhuIyzLiMQUZWKvTMpK1+ypobLWzeJqrjEdRFJ65cRLtDie/+WCfxqsTg1Frh4Mdx+u6vSIQEmTkT9+aQr2tnV+8V9Dn9N9A8POrxnF2x6CwYKPfTmgJDTay7M6LmJQcxX2v72LjEf32DNIBuRgihAekJ3ZOIulDH4yS2hZqm9v9roGnW3ZqNG12J4cq+jd9RYj+stTZMBoUkoaEeucEzzwD//u/ff6x+M6LZH3NxPImXQUwABRFMSqKshuoAtYBRYBVVVV3rn4ZkNz5+2SgFKDz/nogtuvt3fyM3zhY0cCuEivfCdDmnV2ZQ4KYkxbH2n0VuvywtXxHGSlDw7i4S6r+yNgIfnplOh/vq+CTfX2bqSzEQO08Xke73XnevggThg3hwasyWLO3gvd3n/Dx6rQXZjKiwqkxs8nRYSxdlMXCHL97KzjFHBLEP79/EWPiI7jn1R3sPK7PjDWtycUQITwjITKEyNAgjlT1/sN8Xomr/MLfGni6TU6JAqDAIn0wxGl3vLSNO17a5tVzlNW1kDQklCCjFz+eqyqcPNmnH5EMjF5QVdWhquoUIAXXRmFCdw/r/LW7T/VqD7efQVGUexVF2aEoyo6TffzH9IU3t5ViMhpYFKDNO8+Wm5mIxWpj34kGrZdyBovVxqaiam6amoLBcOZT655LxzA+KZJfvb+PxtYOjVYoBqNNhdUEGRRmjD5/Y8d7545h+sihPPH+Xr/o/eBJL24sZkRMOFNHDGXm6Bg2PTbPr4MXbtHhJl77wUySokK58+Xt7JVNdrfkYogQA6coSmcjz95nYOwutRJuMjIuMdKLK/OeETHhRIUFky99MEQXrR0Or0/hsFht3ikf6eqee2DGDFcgo5fCTUGEBRulB0ZvqKpqBT4HLgaiFUUJ6rwrBXBfTiwDUgE6748Carve3s3PdD3H/6mqOl1V1enx8fHe+GP0m63dwYpdZVw9KYmYAG3eebYrJyRiUOATndV3v7uzDFWFm6edG0gKNhpYuiiLysZW/vDJYQ1WJwarTUU1TE6NxhwSdN7HGA0Kf7hlMg6nysPv7MGp0ya5nrarpI6dx+u4a/YorZfiFfGRIfzr7pkMCQ3mey9to7APV0cHC7kYIoRnpCdEUnSy9wGMvJI6slOiMBr8M3NYURSyU6LYUyrBYeFbljqbZyeQdGfGDDh2DPb3bVJdrNlEtQQwuqcoSryiKNGdvw8DrgQOAJ8BN3c+7A7g/c7ff9D5PZ33b1Bd9QcfAN/ubMw1GkgHvJv342GrC8ppaA385p1dxZpDmD4qRlflGE6nyjs7y5g1JpbUmPBuH5MzYijfu3gk/9xyjN2lErEX3ldv66CgzMrssRceqzkyNoJfXjeRTYU1vLrlmNfXpgfLNhYTGRrE4umpF36wn0qODuNfd8/EoCjc9uJWSmtbtF6SLg32iyFCDFR6opnqpvZeNYRu7XCw70SD3/a/cMtOieJQZaPXr7gL4dbhcFLR0EqKtxuNX3ed69cPP+zTj8WZQ2SMag+GAZ8pipIPbAfWqaq6CngUeFBRlEJcaZ3LOh+/DIjtvP1B4DEAVVX3AW8D+4GPgftUVfWrV6E3tpUwOi6Ci8cEdvPOs+VmJnGwopHjNc1aLwWAbcdqKaltYfH0nst4HsrNIDEylMfezZfO1cLrth6twanCJb0c6XXrjFQuz4hn6ZqDFPahGZs/Kq1tYU1BOd+ZOYKIHrJTAsHouAhev3smbXYn33nxa5mI1EkuhgjhOWMTXI08e/Pese9EPXan6rcTSNyyU6JxOFXdlTSLwFVR34pT9dIEkq6Sk2HqVFi1qk8/Fmc2US09MLqnqmq+qqo5qqpmq6o6SVXVpzpvP6qq6gxVVdNUVV2sqmpb5+2tnd+ndd5/tMuxnlFVdayqqhmqqq7R6s/UH4crG9lxvI5bZ6QGfPPOs82fmAi4pn7owfIdZZhDgrhmUs9jhyJDg3nyhkwOVjTy4lfFPlqdGKw2F9UQGmzodZM0RVH43U3ZhJmM/Pzt3dgDOMj2z83HMCgKd14yCoArJiRwxYQEbRflRRlJkfzz+zOoa+7gu8u26qpGVUNyMUQID0lPcE8iuXCpmruB5xQ/beDpNjnFtX7pgyHcvL2XKKtz9SlLGdp9trdHLVgAW7ZAde+nmcVGhEgJiejZv7eWYDIauHla4KY/n09qTDgThw3RxTjVpjY7qwvKWZA9jDDThSfn5WYmkZuZyH99elg3GSQiMG0qrOaiUTGEBPV+omPCkFCeWZjFnrJ6/vezIi+uTjsNrR28ub2U67KHMSzKdRXj3rljuXfuWI1X5l2TU6NZdsd0Smtb+N5L26i3De6GwnIxRAjPGR4VRrjJ2KtGnnmlVpKjw0iI9NIYSB9JigolITKEgjLpgyFcvLmXWJln4f+9vhOAB9/ezco8i1fOc8rtt8Nbb0FERK9/JC7SRG1zu256qUkAQ2daO1zNO3MHUfPOs+VmJrGrpI6qRm3ToVfnl2PrcFywfKSrJ785iSCDgV+u3KvLcbDC/1U1tnKkqolLxvaufKSr67KHsXDKcP5nw5GAvLL09vZSmtrs/GDOaK2X4nMzx8Tyj9uncbiykbte2U5Lu/3CPySEEBdgMCikJZh7VUKyu8Tqt+NTz5adEsWeAHyfFPqyMs/CkhUFWFtcFx4qG9pYsqLAu0GMtDS4+WYI6325SmxECA6nqpsLJBLA0JnTzTsHX/aFW+6kRFQV1u+v0nQdy3eWMiY+gql9aEaVFBXKI1dn8NWRalbu9nIEVQxKW4pqAJidduEGnt158puTiDOH8ODbewKqQZnd4eTlTceYMTqG7JTTG+hv/WML3/rHFg1X5jvfyEjgv7+dQ15JHfe8uiOg/n2FENrpTQCjqqEVi9Xm9w083bJTojla3Uxjqz4+sAlteWsv8fzaQ9jOeq+2dTh4fu0hj5/rDMePw/PPQ0fvnt+xZtdF9ZpmfZSRSABDZ97YVsKo2HBmjenfh5NAkJEYycjYcE37YBRXN7P9WB03T0vpcx+S22aOJGdENE+vOkCdjjr2isCwqbCaIaFBZA6P6tfPR4UH8/zibAqrmnjuYy+/QfrQx/sqsFht3D0Isy+6uiZrGM/dPJlNhTX8+N950lRYCDFgaQlmKhpaaejhw3xe5xS2KX7ewNMtOyUKVYUCi5SRCO85YbX16XaPycuDRx6BjRt79fB4cwgAJxv18blGAhg6cqSyke3H6rh1xohB17yzK0VRyM1MYnNRdY9vlt70zs5SDArcNLX35SNuRoPC0kVZNNg6eGb1AS+sTgxWqqqyqbCGWWNjMRr6/xpxaXo8d8wayUubitlc1PsmTnqlqiovfFXMqNhwrpiQqPVyNHfztBSeuiGT9QcqeWj5Hhw6qVkVQvin9IRIoOdJJHklVoKNCpnDh/hqWV6VfaqRpwQwhPcMP8/Y1PPd7jFXXgkmU6+nkcR2BjAkA0Oc441tpQQbFW6a1vcPzYEmNzORDofKZwd9X0bicKq8u9PC3HHxJA7pXyOq8UlDuHfuGN7ZWcbmQv//gCj0oaS2BYvVxuxejk/tyWPXTGBMXAQPvb1Hs0Chp+wqqWNPqZUfzBk9oMBOIPnerFE8cnUG7+8+wS9XFkhPHiFEv7knkRT20Mgzr6SOicOjCA3ufXNpPYuJMJEaExaQ/aKEfjycm0Gw8cx9S1iwkYdzM7x7YrMZLr+8DwGMzhISnYxSlQCGTrR2OHh3VxnzM5OI64xyDWY5qUOJM4fwiQbTSDYWVlPR0MriAU6Buf+KdEbGhvP4ewVSiy48YlOhq/9Ffxp4ni3MZOQPt0ymsrGNJz/YP+DjaenFr4qJCguW4O9ZfvSNNO67fCxvbCvlmY8OSBBDCNEvqTHhmIIM5x2lanc4KbDUkxMg5SNu2cnRgyYDY2WehdnPbmD0Yx8x+9kN3p+EIQBYmJPM8KhQggwKCpAcHcbSRVkszEn2/skXLIDDh11fFzA03IRBQTejVCWAoRMf762g3tbBd2aM0HopumAwKFw1MZHPD1X5/MP/8h2lRIcHc+XEgc17Dg028tsbszhW08JfNhR6aHViMNtUVE3ikBDGxvd+9FVPckYM5b5vjOXdXWV8vFe7njMDUVLTwtp9Fdw2cwThpqBz7l+QPYwF2cM0WJk+PDQ/gzsvGcWLG4v5r0+PaL0cIYQfMhoUxsafv5Hn4comWtodATOBxC07JYqyOhs1OvnQ5i3uSRgWqw0VsFht3p+E4We8tZcor7dxvNbGz65Mp/jZ69j02DzfBC/AFcAIDoY9ey74UKNBISbCRLVkYIiu/i3NO8+Rm5lIc7vDpzX69S0dfLK/khsmDyckaOBpkLPT4lg0NZm/f1HEoYrur1wI0RtOp8qWohpmj43zaI+cH89LZ1LyEB5/r4CTjf63SXt5czFGg8Idl4zq9v7bZ43i9lnd3zcYKIrCrxZM5OZpKfx5/RFe/Oqo1ksSQvihtAQzR84TwMgrrQNc2bOB5FQfjABv5KnZJAw/4q29xNrOi0fXZGlwoWXUKKithcWLe/Xw2IgQ3QTzJIChA4VVTWwrruXbM0ZgkPrtUy4ZG0dkSBBr9/qujOSDPRba7apH2QwAACAASURBVE4WT/fcGNtfXjeRyNAglqzIxynN9EQ/HaxopLa5nUs80P+iK1OQgT/dMoWmNjtLVuT7VZlBva2Dt7eXcn328PP2q7G1O7C1+2kJV3k5XHYZVAwsO8ZgUHh2URbXZiXxnx8d4I1tJR49vhAi8KUnmCmrs9HSbj/nvrwSK7GdPSMCSVZKFIoC+aWBHcDQbBKGH/HWXmL13grGJZoZG2/2+LF7xdz788aaTVJCIk57c1sJwUaFm6V++wymIAOXj09g/YFKn3XRX76zjPFJkR7toh0TYeKJBRPZVWLldfcHByH6yJ2JdMlYz2dppSdG8khuBusPVLF8Z5nHj+8tb24robndwV09jE698+Vt3PnyNh+uyoOefto14uzppwd8qCCjgT9/K4dvZMTz+HsFPLGygBU3/hDnl1/x7sIfSqqwEKJH7kaeRVXN59y3u9RKzojogJugZw4JYmy8OeAbeZ5v4kVcpPTkc/PGXqKqsZXtx2q5ZpKGZa7l5TBnDixffsGHxplDqGnWRwnJuQXDwqdaOxy8s6uM+ROleWd3cjOT+GDPCXYcq2Wml8trDlU0kl9WzxMLJnr8TfjGnGTe3VXGc2sOMn9iYr+nm4jBa3NRDaPjIrw2Wuuu2aNZf6CSpz7cz6wxsaTGhHvlPJ7S4XDyyuZjzBoTy6TkKK2X43nl5fDyy+B0wj/+AU1NEBEBigLp6fCzn7ke9/TTUNU5rUlRXF+ZmXDvva7bfv1raGgARcEEvKiqPF8RzorGWn6xcy0GVK7d+THzX/0CuMx3tbdCCL+SnugKYBypaiQr5fRrbr2tg8KqJhZOGa7V0rwqOzmKrwqrUVU14AI0bj+/Kp2fv5NP1wRMBahtauP1rcf5zowRAftn19In+ypRVbhWi/IRt4QEOHgQPvjggqUksWaTTCERLmv3VWBt6eBWad7Zrcsy4jEFGVjrg2kky3eUEmRQvPImrCgKzyzMot3h5Dcf7PP48UVg63A42Xq0xivZF24Gg8LvF08G4OfL9+i+3Gl1QTnl9a3cfen5sy/82tNPu4IXAA4HvP226wrJ22/D55+fftyHH8Lrr7u+XnsNXn0VNmw4ff8bb8CyZfDii/DCCwS9+CJZR3Zy/6Y3UFTX8Q2qk3u+eF3qnYUQ5zUyNoIgg3JOI889pa7shJwRgdX/wi07JYqTjW1UNLRqvRSvqW5uR1VhaHjwqUkYTy+cxOz0eH7x3l4efHtPt6VDYmDW7C1nTFwE4xI1Kh8BMBrh2mth9WrXXqMHceYQmtrsupisKAEMjf17awkjYsK9+sHEn5lDgpiTFsfafRVerc3vcDhZudvCFRMSiPVSJsyouAh+emU6a/ZWsG6/78fDCv+VX2alud3BbA/3vzhbytBwfn39RLYV17JsY7FXzzUQqqqybGMxY+IjuDxjYNOCdMmdfdHe5UqHokBBAZw8CStXnr592zZXE67aWqirc329+ebp+w8fdmVgNDRAYyM0NvLUxbexeO+nhDhcG9IQh53FBetpLzvhoz+gEMLfBBsNjI6LOKeRZ16JFUVxfdAPRNmdo2H3BGgfjOLqZv7wyWGunJDIrieuOjUJ47sXj+SVOy/iwavGsXK3hRv+sum8U2hE39U2t/P10VquyUrSPrtlwQLXHuLrr3t8WJzZBOhjlKoEMDRUdLKJrcW1fHtGqjTv7EFuZiIWq4395Q1eO8dnB6uobmpn8TTPNe/szj2XjmF8UiS/en8vTW0SzRa9s6mwBkXBJ1OKbp6WwlUTE3l+7SHdTs7ZfqyO/LJ6fjBndGC+dnbNvnBzODzSCwPgsR3LT2VfuBlUJ4/tuHANrBBi8EpLOHeU6u7SOsYlRBIZGqzRqrxr4rAhBBmUgOyD4XSqPPpuPqYgA8/cOOmcD9IGg8L9V6Tz2l0zqW1u55t/2cgHeyTQ7Qnr9lfgcKra9r9wmz8fgoJg1aoeHxYb4brAq4cyEglgaOjNbSUEGaR554VcOSERg4JXy0iW7ywjzhzCNzLivXYOcF3B+O2iLCoaWvm9pGuLXtpUWM3EYUMYGmHy+rkURWHpoiwiQ4N48O3dtNudF/4hH3vxq6MMDQ9mUc6FXztvnpbif6+xW7acmX0Bru83b/bI4edZj57KvnALcdi5wlrkkeMLIQJTeoKZ4zXNp1LIVVUlr9TKlM4shUAUGmwkIymS/LLAy8B4fVsJ24preeK6iT32ZpuTHsdH91/KxGFDuP+NPH71/l7a7NqXEfiSp/cSqwsqSI0J8+jQgH6LjoYHHoDJk3t8WGxnBkZNs/YZGNLEUyNtdgfv7CzjqomJJERKQ8eexJpDmD4qhk/2VfDgVeM8fvzqpjY+O1jFXXNGE2T0fkxv6oihfO/ikfxzyzEW5iQH9Bu/GDhbu4O8Eit3zh7ls3PGmUNYuiiLe1/byX2v72R/eSMnrDaGR4fxcG6Gpo0ej1U3s+5AJT++PI0wk/GCj/fkSGSfycvz6uGjD+5lZZ6F59ce0s2/qxBC/9ISI3GqrrKDCcOGcKymBWtLBzkjAnsfk50SxUf55QHVyLOsroVnVx/g0vQ4Fk+/8AfzpKhQ3rj3Yp77+CAvfFXMnlIr/3vbVFKG6rvht6d4ci9R39LB5qJq7po9Wj/Pp+eeu+BD3MMmqhslA2PQWruvkrqWDr4zU5p39kZuZhIHKxo5XnPu+K6BWplnwe5UWezDq7QP5WaQGBnKkhUFdDj0d4Vb6Mf2Y7W0O5w+75MzPzOJGaOGsu5AFRarDRWwWG0sWVGg6cjNlzcVE2wwcPuskb16fG1zO7U6GfulJwtzktn02LxT9c4SvBBCXIh7lKq7D0ZeSR0QuA083bJTomlotXO8pkXrpXiEqqo8/t5eVOC3N2b1+kN0sNHAL66byN+/O5WjJ5u57r838tnBKu8uVic8uZdYf6CSDofK1ZOSPHI8j6muhkPnzw53Z2BU6yADQwIYGnljawmpMWHMHuvdpnyBYv7ERMA1tcWTVFVl+Y4yJqdGk54Y6dFj9yQyNJgnb8jkQHmDrpslCu1tKqomyKAwY3SMz89dWmc75zZbh0OzaRXWlnbe3lHGN6cM73Xm2v/7107+3792enllQggR+EbHRWBQONUHY3epFXNIEGkJGk5R8AF3g9I9AdIH491dFr48fJJHrx7fr5HpV08axoc/mcPw6DC+/8p2fr/2EA6dTy4bKE/uJdbsrWB4VKj+MrAvvRR++tPz3h1uCiLcZJQeGIPV0ZNNbDlaw7cvGhGYDei8IDUmnInDhni8D0aBpZ5DlY0+zb5wy81MIjczkT+vP0xJgET1hedtKaohZ0Q04SbfV/xV1Hc/Nu6E9dzAhi/8e1sJtg4HP5gToKNThRBCx0KDjYyICaewytXgOa/ESnZKFMYA38uOS4wkJMgQEH0wqhpaeerDfVw0aii3X9y7TMbujIqL4L0fXcK3pqfyl88KuX3ZVk42an9lXu8aWzv48shJcifpYPrI2a69Fj77DJrOP20m1mySKSSD1VvbSwkyKL2qOROn5WYmsaukjqpGz83iXr6jjJAgA9dPHu6xY/bFk9+cRJDBwC9WFnh1TKzwT/UtHRRY6rlEo0yt4dFhfbrdm9rtTv65+Rhz0uKYMEwHTa+EEGIQSkuI5EhlE7Z2BwfKGwK+/wW4Sicyhw/x+0kkqqryxPt7abU7efam7AFfRA0NNvK7m7N57uZsdh6v47r//optxbUeWm1g2nCwina7k2uzdDB95GwLFrgahq9ff96HxJlDJANjMGqzO1i+s4wrJ0jzzr7KnZSIqsL6/Z6pt2vtcPD+bgu5mUlEhWkz/ispKpRHrs7gqyPVvL9bRlOJM205WoOqwuw0bQIYD+dmEBZ8ZqNMo6LwkBea6V7IRwUnqGxo4weXSvaFEEJoJT3RTHF1M3mlddidKjmpgd3/wi07JZq9lgbsfty3bHVBBWv3VfLAleMYG++5sp9bpqey8r7ZhJuM3PrC1/zjiyK5KHceH++tICEyhGl67BszZw5ERcGHH573IbERIZKBMRh9sq+S2uZ2bpXmnX2WkRjJyNhwj/XBWLe/koZWu+aZMLfNHEnOiGieWrWfOmk2KLrYXFRNWLBRszrJhTnJLF2URXJ0GAoQGRqEQ1XZY6n36eZEVVVe/KqYtAQzl6V7d9SxEEKI80tPMGN3qryf57roMmUQZGCAqw+GrcNB0UnPN5P3hbrmdn79wV6ykqO4xwsXAiYMG8IHP5nD/ImJLF1zkHtf20m9rcPj5/FnLe12PjtURW5mkj5bCAQHw9VXw0cfgbP7QF2c2USNDj6ryBhVH3tjWwnJ0WFcqtEVVX+mKAq5mUm8vKmYhtYOhoQOLGti+c4yhkeFapae72Y0KCxdlMU1f/6K2b/bgK3dIWMNBQCbCquZMToGU5B2seaFOcmnnoeqqvL0qgO8tKmYyNAgfj4/wydr+PpoLftONLB0UVaf3/S/O4AaXyGEEGdKT3A1PF+Vf4LUmLBToxUDXXaKK1Czp8xKRpLvmr57ylOr9mNt6eC1H8wkyOidPcWQ0GD+ettUXtp0jKWrD3D9/2zkr7dNZVJylFfO50ue2Et8fugkrR1OrsnS2fSRrn7zGzCZwND9cyTOHEJtcztOp6ppEEYyMHyouLqZzUU13DojVZ+RNz+Qm5lIh0Md8Nim8nobXx05yU3TUnTRfOpgeSNGg0JLu0M34yqFtirqWyk62czsNN+OT+2Joig8sWAC374olf/ZUMjfvyjyyXmXbTxKbISJG/sR0Lt+8nDNetwIIUSgGZsQAUBzu2PQlI8AjImLIDIkyC/7YGw4WMl7eRZ+dHma13tIKYrCD+aM5q0fXkyHw8miv23mjW0lfl9S4om9xJq9FcRGmJgxyvdT5Xpt/HgYM+a8d8eaTTicKlaNs2skgOFDb24vwWhQuGV6qtZL8Vs5qUOJM4fwyQCnkazYZUFV4WYNpo905/m1h7CfNYJKy3GVQnubi6oBNM8QOpuiKDxzYxbXTx7Os2sO8tqWY14939GTTaw/UMV3Lx5J6Fn9OHrjhNWm2dQUIYQINJ/sq8TYOT3hi8NVg+ZCi8GgMCk5yu8mkTS0dvD4ir2MSzTz48vTfHbeaSNjWPWTOcwcHcOSFQUs/vtmZi39lNGPfcTsZzd4/HmzMs/C7Gc3eO34A91LtHY42HCgkvmZiV7LgPGYtWvhkUe6vSu2M+OqRuM+GFJC4iPtdifv7CjjygkJJAyR5p39ZTAoXDUxkQ92W2jtcPTrA42qqizfUcqM0TGMjI3wwir77nwvivLBa/DaVFjD0PBgJupw4obRoPDHWyZja7fzxPv7iAgJYtFU7wQDX9pUjCnI0O/0zQfe2g3AWz+c5cllCSHEoLMyz8KSFQU4Oq+m19vsLFlRADAoSl6zU6N4aWMxbXYHIUF9339qYenqg1Q1tvL322f7vBw11hzCK9+fwX2v7+TjLhceLVYbj76bz7GaZi4bN/C+Vl8cPsnfPi+ize48dXxPPy8Hupf46kg1ze0Orpmkw+kjZ9u9G55/Hn76U0g+8+8vLsIEwMmmNtITtSulkgCGj6zbX0lNczu3zpDmnQOVm5nIG9tK2FxUzbzxiX3++R3H6zhW08J9PoxEX8jw6DAs3QQrFAU+yi/numw/eMETHqOqKpuLqpk1Nla35WbBRgN/+c5U7nplOw8t30O4ycjVHn5jrmtu552dZdw4JZn4yMFRZy2EEHr1/NpD2DocZ9zmzhYdFAGM5Gg6HCoHyxuZrFFz7b7YXFjNG9tKuHfuGM2agRsNCgWWhnNub7M7+fP6I/x5/RGvnFdvz8s1BeVEhQUza6x+yoLPa8ECeOwxVzPPe+894664SHcGhraNPCWA4SOnmndKB/0Bu2RsHJEhQazdW9mvAMbyHaWEm4y6msH8cG4GS1YUnLExCAkykBAZwn3/3sWq/CSeumGSfIgbJI7VtFBe38p9OisfOVtosJEXvjed7y7byk/eyOPFO4I8cjXF7fWtx2ntcMroVCGE0IHBni2aneJqRplvqdd9AKOl3c6jK/IZFRvOA1f6fvR5Vz09P17+/kUDPv73X97e5/P6UrvdyboDleRmJhGs9/IRgIkTYdQoWLXqnABGbGcGhpSQDALHa5rZWFjNz68ap4uGkf7OFGTg8vEJrD9QicOp9unvtKXd7spoyBpGRIh+nv7uCPHzaw9xwmo7NYVkQfYwXviqmD+tP8yWo1/wm+szuWHKcBRFnkeBbFOhu/+F/iP1ESFBvHLnDG594Wt++NoOXr1rJjNGD7xBVZvdwT+3HGfuuHjGaZimKIQQwuV82aLDo8M0WI3vpQwNIybCRH6pFXQ+4er3aw9TWmvjrXsvJsykbbnL+Z43ydFhXJ6RMODjJ+v8ebmpqJrGVjvX6nn6SFeK4srCWLYMbDYIO/33GB1uwqBAtcYZGH4QBvJ/b24vxWhQWCzNOz0mNzOJmuZ2dhyr7dPPrS6ooLndoct/i4U5yWx6bB7Fz17HpsfmsTAnmSCjgf/3jbGsvn8Oo+Mi+Nlbu7nn1R1UNrRqvVzhRZuLqhkWFcroOH30aLmQqPBgXv3BDIZHh3HXK9s90qX9wz3lnGxs4+45kn0hhBB68HBuBmFn9R4LCzbycK5vRmprTVEUslP038hz5/E6Xt5czO0Xj2TmGO0vhHj7edPd8UODDbp5Xq4pKCcyJIjZafrOqj3DggWuaSQlJWfcbDQoxESEUNOsbQaGBDC8rN3uZPmOUuaNTyApSpp3esplGfGYggys7eM0kuU7ShkVG85Fo/xr9FdaQiTv/Mcl/PK6CXx1pJqr/vgFy3eU+v1YKnEup1NlS1ENl4yN86tMmzhzCK/fPZPo8GC+99I2DlU09vtYqqry4ldHGZdo5tL0gb3h33PpGO659PwjwYQQQvTOwpxkli7KIjk6DAXXle+li7J002fAF7JTojlS1UhLu13rpXSrtcPBo+/mMzwqjEevGa/1cgDvP2/OPj7A3PQ4jz4v+7uX6HA4+WR/JVdMSPCbxq8AzJ8Pe/dCxrlBoDizSfMMDP3k0Aeo9QcqqW5q5zvSvNOjzCFBzEmLY+2+Cp5YMKFXH/RKalrYWlzLQ/PH+dUHQzejQeHuS8dwxYREHn0nn4ffyeejgnJ+e2OWbtLkxMDtL2+grqWD2WnaXzXpq2FRYbx+90wW/30L3122leU/nMWofmSRbC6q4WBFI8/dlD3g/6tXTux7nxwhhBDdW5iTPKgCFmfLTo7CqcJeS4NHyiU97X82HKGwqol/3jUDs85Kpb35vOl6/B+9vpOvDldTb+sgKizYI8fv715i69FarC0dXKOjvnu94t57tbdDcPDp74FYs0nzHhiSgeFl7uadcz3Y2E645GYmYrHa2F9+bnfj7ryzsxRFwWvjHn1ldFwEb957MU9+M5NtxbXM/9OXvLGtRLIxAsTmIlf/C79KNexiZGwEr989E7vDyW0vbu1XE60XvzpKnNnEN6cMH/B6ik42UXSyacDHEUIIIbJTOxt5eqBU0tP2Wur5+xdHuWlqikcbavubH1+eTmObnVc2HfPYMfu7l1i9t5xwk9E//z02bIC4OMjPP+PmOHOI5hkYEsDwopKaFr46Us23LkqV5p1ecOWERAwKvSojcTpV3t1lYU5aXEBkKxgMCndcMoq1P5tLdkoUS1YUcPuybZTWtmi9NDFAmwprGBsfQeIQ/y05S0+M5NW7ZtJg6+C7L26lug+R+sKqRj47dJLvzRpFaPDA0y0fX1HA453z4IUQQoiBSIgMZVhUqO76YHQ4nDzyTj4xESaeWDBB6+VoauLwIVw1MZFlG4/S2NrhkWP2Zy/hcKp8sq+Cy8cneGQ/43OZmdDY6JpG0kVsRIhkYASilXkWZj+7gbnPfwbAkDD9pHAFklhzCNNHxfDJvooLPnZzUQ0Wq02XzTsHIjUmnNfvnskzN05id6mV3D9/yatbjuF0SjaGP2q3O9lWXOu32RddZaVE8dL3L+JEvY3bl22jvqV3m4hlG48REmTgtplSdieEEEJ/XI089ZWB8X9fHmV/eQNP3zCJ6HCT1svR3P3z0mlotfPqluOarWH7sVqqm9q5ZpKfTB85W2IizJhxbgDDbKK53YGt3aHRwiSA4XEr8ywsWVFwxjif3605xMo8i4arCly5mUkcrGjkeE1zj49bvrOUIaFBzA/AenhFUbht5kjWPjCXaSOH8qv393HrC19f8O9E6M/uUiu2DgeXjPX/AAbARaNieOF70ymqauKOl7fR1NZz07OapjZW7Cpj0dQUYs0hPlqlEEII0XvZKdEcq2npdWDe245UNvJf649wXdYwrvbXD8selpUSxeUZ8bz41VGaL7D38JY1BeWEBBk8MipWMwsWwNatUFV16qY4sytA1pfsWk+TAIaHPb/2ELaOMyNStg4Hz689pNGKAps7ILG2hyyMelsHH++t4JtThvtnClcvJUeH8epdM3jupmz2lzeQ++cvWbaxGIdkY/iNzUXVKArM0sHYM0+5ND2e//lODgWWeu755w5aO84fsf/X1yW02Z38YM4o3y1QCCGE6IPslM4+GBbtszAcTpVH3s0nPMTIb76ZqfVydOUnV6RT19LB61t9n4XhdKp8vK+Cb2TEE6GjZqp9tmABqCqsXn3qprjOC0w1zdr1wZAAhoedr2FdfxrZiQtLjQln4rAhPfbBWJV/gja7k8XTAqt8pDuKonDLRamse+AyLhkbx9Or9nPLP7ZQdLLpVGnT6Mc+YvazGyQrSIc2F9YwaXgUUeGe6ZqtF7mZSfx+cTZfF9fwo9d30eFwnvOY1g4Hr319jMsz4klLiNRglUIIIcSFZSdHA+iiD8Yrm4+RV2LlN9dnEh8pmYtdTR0xlEvT4/i/L4/6vNwhr7SOyoY2rpnkZ9NHzjZlCjz1FFx00amb3BmyWvbBkACGh52vQWQgNI7Uq9zMJHaV1FHV2Nrt/ct3lDEu0XwqYj4YJEWFsuyO6fzpW5MprGpi/h+/4KHle7BYbaiAxWpjyYoCCWLoSEu7nbzSOi7xw/GpvXFjTgr/uXASGw5W8cBbu8/JDPpg9wmqm9q5ux9z1nvyk3np/GReukePKYQQYvCKCg9mVGy45n0wSmpa+P3aQ8wbn8ANHpjaFYh+Mi+d6qZ23thWMuDj9GUvsaagApPRwLwJflw+Aq7xqU884Wro2Sk2QkpIzqAoSqqiKJ8pinJAUZR9iqL8tPP2GEVR1imKcqTz16GdtyuKovy3oiiFiqLkK4oytcux7uh8/BFFUe7w1Z/h4dwMws4qUwgLNvJwboavljDo5E5KRFVh/f6qc+4rrGpkd6mVxdNSUZTBNQlGURRuzElh3YNzCQ4yYD/rA6OUNunLtuJaOhwqswOk/0V3bps5ksevHc+q/HKWrMg/1WxWVVVe3HiU8UmRXDLWswGcOelxzEkP3L9TIYQQvpedEq1pBoaqqjz6bj5BBoVnbpw06Pa4vTVjdAwzR8fwjy+LeixhvZC+7CVUVWXN3gouTY9jSGgAZNTa7bB+PRw4AJwuIdFylKquAhiAHfi5qqoTgIuB+xRFmQg8Bnyqqmo68Gnn9wDXAOmdX/cCfwNXwAP4NTATmAH82h308LaFOcksXZRFcnQYCq6+BEsXZbEwJ9kXpx+UMhIjGRkb3m0fjOU7yjAalEH9958QGUpbx7kp+yClTXqyuagGk9HARaNitF6KV907dyz3z0vj7R1l3PnyNmY/+ymjl6zmcGUTOSOiPb4J23einn0ntE/zFUIIETiyU6Ior289b/avt72xrZQtR2tYcu0EhkVJlndPfnpFOpUNbSzfUdrvY/RlL5FfVo/Faguchqrt7XD99fCPfwAQZjISYTJSIwEMF1VVy1VV3dX5+0bgAJAM3AD8s/Nh/wQWdv7+BuBV1eVrIFpRlGFALrBOVdVaVVXrgHXA1b76cyzMSWbTY/MofvY6Nj02b1B/ePYFRVHIzUxic1E1DV3mPdsdTlbkWbg8I2HQ1wVKaZP+bSqsJmdENGGmwG006/bAVeO4LD2OL49UY7Ge3vy9l2fxeFnTUx/u56kP93v0mEIIIQa37JTOPhilvg+Ql9fb+O3qA1wyNpZbZwR+f7eBmjU2lmkjh/K3z4tot3d/Qe9C+rKXWLO3giCDwlWBMvkwPByuuAI+/NDV0BNXH4yaZikhOYeiKKOAHGArkKiqajm4ghyAu6AoGegaTivrvO18t4sAlZuZSIdD5bODp8tIvjh8kpONbSyenqLhyvShu9Km0GCDlDbpRF1zO/vLG5idNjhKHRRFofBk0zm3t3Y4paxJCCGE7k1KHoJBwad9MFzN2D9l1tINNLfZuWJ8gpSO9IKiKNx/RTon6lt5d1eZV8/lKh8pZ9bYWKLDTV49l08tWABHj8LBg4BrlKr0wDiLoihm4F3gZ6qqNvT00G5uU3u4/ezz3Ksoyg5FUXacPHmyf4sVupCTOpQ4cwifdJlGsnxHGbERJuaN9/MGOh5wdmkTwGXp8ZIdpBNbjtagqjA7QBt4dueEtfu0WylrEkIIoXfhpiDSEyLJt/gmA2NlnoUlKwpOZS2qwO8/OSzN2Htpbnock1Oi+Ovnhd1OQvOUA+WNHK9p4dosP58+crYFC1y/fvgh0JmBISUkpymKEowrePG6qqorOm+u7CwNofNX92X2MqBr7lQKcKKH28+gqur/qao6XVXV6fHx8Z79gwifMnSman1+qIrWDge1ze18erCShTnJBBt19zTXRNfSpgXZw/j88EnK6lq0XpbAVT4SYTKeSkkdDKSsSQghhD/LTokiv6weVT3nGqnHPb/2ELazmlBKM/bec2dhlNbavBr0WbO3HIMC8wOlfMQtJcU1UvXLLwF3BoYEMADXVBFgGXBAVdU/drnrA8A9SeQO4P0ut3+vcxrJxUB9Z4nJWmC+oihDR7cQPAAAIABJREFUO5t3zu+8TQSw3MxEmtsdbC6qZmWehQ6HKuUj5/H4tRNQFPjt6gNaL0XgauA5c0zsoAq2ycQmIYQQ/iw7NZra5nbK6ryfOXi+7ETJWuy9eeMTyBw+hL9+XoTdS1kYqwvKmTk6llhzAPbe+/BDeN/1ETw2IoTa5jYcTu8H77qjt93ybOB2YJ6iKLs7v64FngWuUhTlCHBV5/cAq4GjQCHwAvAjAFVVa4Gnge2dX0913iYC2CVj44gMCWLt3kqW7ywjKzmK8UlDtF6WLg2PDuNH30hjdUEFm4uqtV7OoHbCaqO4utnj40P1zlcTmx65OoNHrpagiBBCCM/KTo4C8Po41cOVjZyv1YVkLfaeoij8ZF46xdXNrMov79PP9mYvcaSykaKTzVybFSDTR86WkgJG14WnOLMJpwrWFm2yMII0Oet5qKq6ke77VwBc0c3jVeC+8xzrJeAlz61O6J0pyEB6gpm3OsckRYUFszLPIn0ezuPeuWN4e0cpT324n1U/mUPQILr6ryebi2oAVwBusFmYk+z1/5/TRgb2WFohhBDaGD8skmCjQn6ZleuyvdPzoLCqie+8sJUIk5F2h0pblykakrXYd/MnJpKRGMlfPivk+snDMRp61wS1N3uJ1QUVKArkZgZoAAPgySehqYnY7z4AQE1zuybZJvKJRQSMlXkWCrrMaK63dbBkRYE0ODqP0GAjv7h2AgcrGnljW4nWyxm0NhdWExNhYnxSpNZLCUg7j9ey87gk4AkhhPCskCAjE4YNYY+XJpEUVzfznRe+BlTeu28Ov7sp2+tZi4HOYFD48bw0CquaWLO391kYvdlLrNlbzvSRQ0kYEjrQZerXsWPw4ovEhbgCP9WN2kwikQCGCBjPrz1Eh+PMWixpcNSzqyclccnYWP6w7jB1zdo14xmsVFVlU1E1s8bGYujlVQDRN899fIjnPpbXACGEEJ6XnRLFXksDTg/3Ajhe08yt//c1dqfKv++5mLQE8xnN2Dc9Nk+CF/10bdYwxsZH8JcNhb3+d7vQXuLoySYOVjRyzaQAmz5ytuuvB6uV1AO7AajW6LODBDBEwJAGR32nKAq//v/s3Xd8lfX5//HXlQEJI4QRZpgOcIEiIhRc9auCu9a9sLXi6Py1WkeHWm21td9atf1qabXOuncdiHuBCiIgAsqSlQTCSBhJyLh+f5w7eAiZ5OTc5yTv5+ORR875nPu+z3UO55Drvu7POGk/NpdW8JdpX4YdTpuzZN1WCorLGNcGh4+IiIgku+G52Wwpq2Bp4daYHXPlhm2c+8+PKK2o5OGLD2XvXuqhGUupQS+MhfmbmbagICbHfOXzfCByYbBVO+YYSE+n+zvTAFi/RT0wRJpFyzLunqG9O3P+oQN45KOvWZBXHHY4bUr1BKrj9mxbE3iKiIi0BiOC5c/nxmgYyZpNJZz7rxlsLi3n4YsPZd++moy+JZw0vC+Dunfgzje+iskyuK9+ns+B/bNb/zlH585w5JFkvPoyqSnG+pCWUlUBQ1oNLcu4+/7fMXvTJTOdG1+cH5f1zCXig8WF9MvOZEC3DmGHIiIiIk20R05HMtNTY7ISSX5RKef8cwabtpbz0MWHsn+wyonEXlpqClcctSfz1xTz1qK1zTrWyg3bmLe6qPWuPlLTuedio0fTux0UqgeGSPPEa1nG1ii7Qzt+cexQZizdsKMbnLSsyipn+pL1jNuzO1bX+mgiIiKSsNJSU9i/X/Mn8lxbXMq5/5xB4eYy7v/+aEb0z45RhFKX7xzUj9yumdzxxuJmXbyrngy01c9/Ue2ii+DBB+mc3YnCkHpgJNQyqiLNFY9lGVurc0YP4JGPVvD7lxZw1NCeZLZLbXgn2W3z1xRRXFrBuD01/0VL+u1J+4YdgsSZmfUHHgR6A1XAFHe/w8y6AY8Dg4DlwJnuvtEiFcQ7gOOBbcBF7v5pcKxJwK+DQ9/s7g/E87WISOIbnpvNwzO+pryyivTdWJJ+3eYyzv3XR+QXl/LA90dz8MCuLRCl1JSemsIVR+7Jdc/O472vCjl875w6t60vl3jl83z275dF/7bUm9adoWUbWLE1nHMF9cAQESAyqdENJ+3L6k0l/OPdJWGH0+p9uGQ9AGP30PwXLWm/vl3Yr6+64bYxFcAv3H0fYAzwQzPbF7gGeMPd9wLeCO4DTAT2Cn4mA3cDBAWP64FDgdHA9WamMwsR2cnw3C6UVVTxZcHmJu+7Yet2zv/XR6zauI37LjqEQwZ1a4EIpS7fPbgffbpkNDgXRl25RF5RCbNXbGo7vS+q/eEP/OVXZ7CtMJxl6lXAEJEdDh3SnROG9+Ged5awWqu3tKgPFheyV89O9OzcitcLTwDvf1XI+18Vhh2GxJG751X3oHD3zcACoB9wClDdg+IB4NTg9inAgx4xA8g2sz7AccA0d9/g7huBacCEOL4UEUkC30zk2bR5MDZtixQvlq/fyr2TDmHMEF3QiLf2aalcfuQezPx6I9OXrq9zu7pyiVeDYdcTW/vqIzUdcQSplRXsM3dGKE+vAoaI7OS64/cB4A8vLwg5ktarrKKST5Zv0PCROLjrza+4682vwg5DQmJmg4CDgI+AXu6eB5EiB9Az2KwfsDJqt1VBW13tIiI7DOzegayMtCatRFJUUs4F937M4rVbmHLhKOUDITpzVH96dm7PnW/UnSvUlUu8Mi+fYb07MySnU0uGmHjGjKE0K5vxiz5i2/aKuD+9ChgispN+2ZlcfsSevDQ3jxn1VKNl981esYnS8iq+peEjIi3GzDoBTwM/c/f61oiubRZdr6e95vNMNrOZZjZz3bp1uxesiCQtM2N4bjZzVjauB0ZxaTkX3vcxC/OLueeCkRxRz9wL0vIy0lO59Ig9mLF0Ax8va/yQiLWbS/nk6w1MaGu9LwDS0ij41lEcuXQm64vi32NbBQwR2cWlRwyhX3YmN7wwn4rKqrDDaXU+XFxIikWG7IhI7JlZOpHixSPu/kzQXBAMDSH4Xb123iqgf9TuucCaetp34u5T3H2Uu4/KydGJiEhbNDy3C4sKNlNaXlnvdlvKKrjovo+Zv7qIv587km8P6xWnCKU+544eQI9O7ZrUY3Pq/ALc4fgD2tj8F4HN/3McPbYVse2D6XF/bhUwRGQXGemp/OqEfViYv5lHP1nZ8A7SJB8sWc8Budl0yUwPOxSRVidYVeReYIG7/yXqoReAScHtScDzUe0XWsQYoCgYYjIVONbMugaTdx4btImI7GR4bjaVVc4XeXV39tq2vYLv//sT5qwq4q5zDuLY/drglfsEldkulUsOG8J7XxXy6YqNjdrnlXl57JHTkb16trHhIwE/bgKXn3INa/rtEffnVgFDRGo1cf/ejB3Snf99bRGbtoWzznNrtKWsgjkrNzFOw0dEWso44ALg22b2WfBzPHArcIyZfQUcE9wHeBlYCiwG/glcAeDuG4CbgE+Cn98FbSIiOxnRP7JCxdyVtc+DUbK9kovvn8nMrzfw17MOZGIbvWqfyM4fM5CuHdK5q565MKqt31LGR8s2MHH/PkRq5m1P1345vDJsPGuJ/8W4tLg/o4gkBTPj+pP35fg73uP2aV9y4yn7hxbLc7NXc9vURazZVELf7EyuOm4opx6UnHPpfbxsPRVVrgm74uQPpx0QdggSZ+7+PrXPXwFwdC3bO/DDOo51H3Bf7KITkdaod1YGPTq1r3UlktLySiY/NJMZy9bzlzNHcNKIviFEKA3p2D6NHxw2hNumLmLeqiIOyP1m2dSaucS0LwqorHImHtB2e9H06NSeHls3MmDKXdDnp5CbG7fnVg8MEanTsN5ZnD9mIA/N+JqF+fXNgddynpu9mmufmcfqTSU4sHpTCdc+M4/nZq8OJZ7m+mDxetqlpXDwwK5hh9Im7JHTiT3a2uzgIiISV2bGiNwuzKmxEklZRSWXPjSL9xcX8qfvDuc7B8XvJE+a7sKxA8nKSNtlLoyaucTLn+czoFsH9u2TFe8QE0ZGeiq9q0oZ+68/w1FHQX5+3J5bBQwRqdfPj9mbrMx0bnzhCyIXKuPrtqmLKKkxKVZJeSW3TV0U91hi4cMl6xk1sCsZ6alhh9ImvP5FAa9/URB2GCIi0soNz81maeFWNpeWA7C9ooorHv6Ud75cxy3fOYAzRvVv4AgSts4Z6Xxv3GBe+6KABVHzmUTnEkXbyvlwcSETD+jdZoePVNs8cAjbMjvC4sVw001xe14VMESkXtkd2vGLY4cyfel6Xv08ftXVams21b4805pNJaEUVJpj/ZYyFuQVa/nUOPrne0v553tLww5DRERaueH9u+AO81YXUV5ZxY8f/ZQ3Fq7lplP35+zRA8IOTxrp++MG06l9Gn97c/GOtuhcYtqCAiqqnOP31zwme1Zupn1ZaeTOv/8dt14YKmCISIPOHT2AYb07c/NLCxpcIixW3J0X56yhruK2AxPveI8HPlxOUUl5XGJqrulL1wPwLc1/ISIi0qqs3rgNgHP/+RHDb5jK1PkFXH/SvlwwZmDIkUlTdOmQzqRvDeTlz/P4qmDzLo+/Mi+PftmZDI+aI6OtuuD1h6isTtQrK+PWC0MFDBFpUGqKccPJ+7F6Uwn/eKflr2av3VzKZQ/P4sePzqZf10zap+38X1VGegpnjMolPTWF61+Yz6F/eJ2fP/EZnyzfkNC9Mj5YvJ7O7dMY3k9/9ERERFqL52av5vcvLdxxv6S8irQUo2uHdiFGJbvr4vFDyExP5W9vLd6pfXNpOe99VciE/TV8hLw8xr3/X9pVVkTub98et14YKmCISKOMGdKdEw7ow93vLGZ1HcM6msvdeebTVRzzl3d5a9E6rp04jLd+cSR//O5w+mVnYkC/7ExuPW04t50+ghd/PJ7//ng83x2Zy2vzCzjjnukcc/u7/Ou9pWzcmnhLv364pJBDh3QjLVX/9YqIiLQWtc3XVVHlSTtfV1vXrWM7LhgzkBfnrGHpui072t9cuJbtlVVM3L/trj6yw003YVVVO7fFqReGllEVkUa79vhhvL6ggFteXsDfzh0Z02PnF5Xyq2fn8cbCtRw8sCt/On34jhmfTz2oX53Lpu7frwu//84BXHf8Prw0N49HP1nBzS8t4E+vLmLC/r05e3R/xg7pHnqlfNXGbXy9fhuTxg4KNQ4RERGJrfrm65Lk9IPDhvDA9OX8/a0lO9pemZdPz87tGTlAK8kxfTppFTWGcG/fDh9+2OJPrQKGiDRabtcOXH7kHvz19a+4YMx6Dh3S/Mko3Z0nZ67ippe+oLyyit+euC+TvjWI1JSmFRw6tk/jzEP6c+Yh/VmYX8xjH6/kmU9X8cKcNQzq3oGzRw/guyNzyencvtkx744PF0fmvxin+S/i6vazDgw7BBERaeX6ZmfW2ju1b3ZmCNFILOR0bs+5owfywPTlPHbJGLp2TOeEO9/n7EP6k9LEHLVVmj2b/85dw4/+M5upPzucob07x+2p1Y9ZRJrk0sP3oF92Jje8+AWVVc2bb2L1phIm/fsTfvn0XPbtk8XUnx3O98cPbnLxoqZhvbO44eT9+PhX/8NfzhxBz84Z3PrKQsbe8gaXPzyLd79cR1UzY2+qD5YU0qNTe/bu1anhjSVm+mZnKoEUEZEWddVxQ8mssTx6ZnoqVx03NKSIJBYuPWIIqSnG05+uYlH+Fsoqqpig1Ud26N4xclFw/ZayuD6vemCISJNktkvluuP34Yf/+ZRHP17B+bsxu7a785+PV3DLywupcuemU/bjvEMHxryinZGeymkjczltZC6L127msY9X8vSnq3jl83xyu2Zy1qhIj43pS9Zz29RFrNlUQt/sTK46bmidQ1aa6rnZq7lt6kJWbyolMz2V5z9bE7NjS8NenLMGgJNG9A05EhERaa2q/663VC4h4eiVlcHoQV157JOVPPbJSlIM8jQsaIeczpFJatepgCEiie74A3pz6OBu/O9rizhpeF+6dEhv9L4rN2zj6qfn8uGS9Yzfswe3nHYA/bt1aMFoI/bs2Zlfn7gvV00YytT5BTz28Qr+d9qX/GXal5hBdYeM1ZtKuPrpueQXlXD0Pr2a9ZxvLCjg9te/oqwiMslRSXkl1z4zD0BJTZw8PONrQAUMERFpWfXN1yXJ6bnZq/lk+cYd96scfvXc56SkmP6tie6BEd+J81XAEJEmM4ssq3rCne9x++tfcsPJ+zW4T1WV89CMr/njqwtJMeOW0w7g7EP6x31yzfZpqZw8oi8nj+jL8sKtnHjX+2wpq9hpm7KKKm59dRG3vhr72cNLyiu5beoi/eETERERSWC3TV204yJUNeVx3+iSmU5qirF+q3pgiEgS2KdPFucdOpCHZnzNOaMH1Dt5z7LCrVz91Fw+Xr6BI/bO4ZbTDkiIeQkG9ejI1hrFi2h/O/egZh3/R/+ZXWu7ZiUXERERSWxaXaZ+KSlGt47tKNysHhgikiR+fszevDBnDTe+OJ9HfnDoLr0pKqucf3+wjD+/toh2qSncdvpwTj84N/QlTaPVNXN4v+xMThzevGEHt7y8ULOSi4iIiCQhrS7TsB6d2se9B4ZWIRGR3da1Yzt+cezefLhkPVPn5+/02OK1Wzjjng+5+aUFjN+zB9N+fgRnjIr/kJGGtOTM4ZqVXERERCQ5KY9rWI9O7SjUHBgikkzOHT2Au99azA//M5uqqk/pk53ByAFdee2LAjq0S+WOsw/k5BF9E65wUa0lZw7XrOThu/v8g8MOQURERJJQdb526ysLyS8upZ/yuJ08N3s1M5dvpKS8knG3vhm398bcvcWfJBmMGjXKZ86cGXYYIknnudmr+eXTc9leY5Kj4blZ3DtpNDmd24cUmYiEwcxmufuosOMIg3IJERFpC56bvZprn5lHSXnljrbM9FRuOe2AmBUx6sonNIRERJrltqmLdileAKzfUq7ihYTuyZkreXLmyrDDEBERkSSlXGJXt01dtFPxAr5ZoaWlqYAhIs2iGZolkT01axVPzVoVdhgiIiKSpJRL7CrM/F8FDBFplrpmYtYMzSIiIiIirU+Y+b8KGCLSLJqhWURERESk7Qgz/9cqJCLSLFppQ0RERESk7Qgz/1cBQ0Sa7dSD+qlgISIiIiLSRoSV/6uAISIirdb93xsddggiIiKSxJRLJBYVMEREpNXKbJfa8EYiIiIidVAukVgSahJPM7vPzNaa2edRbd3MbJqZfRX87hq0m5ndaWaLzWyumY2M2mdSsP1XZjYpjNciIiLhe2j6ch6avjzkKERERCRZKZdILAlVwADuBybUaLsGeMPd9wLeCO4DTAT2Cn4mA3dDpOABXA8cCowGrq8ueoiISNvy37l5/HduXthhiIiISJJSLpFYEqqA4e7vAhtqNJ8CPBDcfgA4Nar9QY+YAWSbWR/gOGCau29w943ANHYtioiIiIiIiIhIEkmoAkYderl7HkDwu2fQ3g9YGbXdqqCtrvZdmNlkM5tpZjPXrVsX88BFREREREREJDaSoYBRF6ulzetp37XRfYq7j3L3UTk5OTENTkRERERERERiJxkKGAXB0BCC32uD9lVA/6jtcoE19bSLiIiIiIiISJIy91o7J4TGzAYB/3X3/YP7twHr3f1WM7sG6ObuvzSzE4AfAccTmbDzTncfHUziOQuoXpXkU+Bgd685t0bN510HfB3jl9MDKIzxMeMpmeNX7OFI5tghueNX7OFJ5vhbIvaB7t4muzW2UC4B+oyFJZljh+SOX7GHI5ljh+SOX7HvqtZ8Iq0Fnmi3mdmjwJFADzNbRWQ1kVuBJ8zsYmAFcEaw+ctEiheLgW3A9wDcfYOZ3QR8Emz3u4aKF8F+MU+2zGymu4+K9XHjJZnjV+zhSObYIbnjV+zhSeb4kzn2RNRShZtk/ndS7OFJ5vgVeziSOXZI7vgVe+MlVAHD3c+p46Gja9nWgR/WcZz7gPtiGJqIiIiIiIiIhCgZ5sAQERERERERkTZOBYyWNSXsAJopmeNX7OFI5tghueNX7OFJ5viTOfa2JJn/nRR7eJI5fsUejmSOHZI7fsXeSAk3iaeIiIiIiIiISE3qgSEiIiIiIiIiCU8FDBERERERERFJeCpgNIOZ3Wdma83s86i228xsoZnNNbNnzSw76rFrzWyxmS0ys+PCiXpHLI2O3cyOMbNZZjYv+P3t8CJv+vsePD7AzLaY2ZXxj3hnu/G5GW5m081sfvBvkBFO5E3+3KSb2QNBzAvM7Nqw4g7iqS32m4K4PzOz18ysb9BuZnZn8H2da2Yjw4t8R6xNif+8oH2umX1oZiPCi7xpsUc9foiZVZrZ6fGPeKc4mhS7mR0ZtM83s3fCifobTfzcdDGzF81sThD/98KLvO1I5lwiiEf5RAiUS4QnmfMJ5RLhSeZ8IuFyCXfXz27+AIcDI4HPo9qOBdKC238E/hjc3heYA7QHBgNLgNQkif0goG9we39gdbK871GPPw08CVyZZJ+bNGAuMCK43z2JPjfnAo8FtzsAy4FBCRZ7VtTtnwD3BLePB14BDBgDfJSgn5u64v8W0DW4PTHs+JsSe3A/FXgTeBk4PVliB7KBL4ABwf2eSfa5uS7q+5sDbADahf0aWvtPE/9fTahcYjfiVz4RzvuuXKLl40+KfKKJsSuXCO+9T6h8oomxt3guoR4YzeDu7xL5R4lue83dK4K7M4Dc4PYpRP4DLnP3ZcBiYHTcgq2hKbG7+2x3XxO0zwcyzKx93IKtoYnvO2Z2KrCUSOyha2L8xwJz3X1OsN16d6+MW7A1NDF2BzqaWRqQCWwHiuMVa011xB4dT0ciMUPk+/qgR8wAss2sT3wirV1T4nf3D919Y9C+0/chDE187wF+TOQkYW3LR1e/JsZ+LvCMu68Itku2+B3obGYGdAr2q0BaVDLnEqB8IizKJcKTzPmEconwJHM+kWi5RFosDya7+D7weHC7H5Evf7VVQVuiio492neB2e5eFud4mmJH7GbWEbgaOAYIffhII0W/93sDbmZTiVQxH3P3P4UWWcOiY3+KyB/uPCJXTf6fu2+oa8ewmNnvgQuBIuCooLkfsDJqs+rva158o2tYHfFHu5jI1Z+EU1vsZtYP+A7wbeCQ8KKrXx3v+95Aupm9DXQG7nD3B8OJsH51xP834AVgDZH4z3L3qnAilCjJnEuA8omwKJeIs2TOJ5RLhCeZ84mwcgn1wGghZvYrItWmR6qbatksIdewrSX26vb9iHTruzSMuBqjlthvBG539y3hRdV4tcSfBowHzgt+f8fMjg4pvHrVEvtooBLoS6Sr8y/MbEhI4dXJ3X/l7v2JxP2joDlpvq91xA+AmR1FJOm4OozYGlJH7H8Frg7z6mBj1BF7GnAwcAJwHPAbM9s7pBDrVUf8xwGfEfnOHgj8zcyyQgpRSO5cApRPhEW5RDiSOZ9QLhGeZM4nwsolVMBoAWY2CTgROM/dq/+TWgX0j9osl0hlKqHUETtmlgs8C1zo7kvCiq8+dcR+KPAnM1sO/Ay4zsx+VMchQlXP5+Yddy90921ExvGFPqFkTXXEfi7wqruXB13fPgBGhRVjI/yHyBVBSJLvaw3R8WNmw4F/Aae4+/rQomqc6NhHAY8F39nTgf8Lum0nqpqfm1fdfau7FwLvAqFOetYI0fF/j0iXVXf3xcAyYFhokbVxyZxLgPKJsCiXSAjJnE8olwhPMucTcc0lVMCIMTObQKRCeXLwR6LaC8DZZtbezAYDewEfhxFjXeqK3SIzQb8EXOvuH4QVX33qit3dD3P3Qe4+iEg19g/u/reQwqxTPZ+bqcBwM+sQjP88gsikPgmjnthXAN+2iI5EJq9aGEaMdTGzvaLunsw38b0AXBjEPgYocveE6u4JdcdvZgOAZ4AL3P3LMGJrSF2xu/vgqO/sU8AV7v5cCCHWqZ7PzfPAYWaWZmYdiJzwLIh3fA2pJ/4VwNHBNr2AoUTG+0ucJXMuAconwqJcIjzJnE8olwhPMucToeYSHvKMrMn8AzxKZAxbOZFK2cVEJtRaSaTrzGfsPBvur4jMGL4ImJgssQO/BrZGtX9GiLPhNvV9j9rvBhJjFZKmfm7OJzJh2OfAn5IldiIT9zwZxP4FcFUCxv508L7OBV4E+gXbGvD34Ps6DxiVoJ+buuL/F7Ax6t9kZrLEXmO/+wl/FZImxQ5cFXzePwd+lmSfm77Aa8Fn/nPg/LDjbws/u/E3IWFyiabGj/KJMD83yiVaNv6kyCeaGLtyiRDjJ4HyiSZ+blo8l7DgiUREREREREREEpaGkIiIiIiIiIhIwlMBQ0REREREREQSngoYIiIiIiIiIpLwVMAQERERERERkYSnAoaIiIiIiIiIJDwVMESkVTCzt81sVNhxiIiISPJSPiGS2FTAEBEREREREZGEpwKGiITCzH5pZj8Jbt9uZm8Gt482s4fN7Fgzm25mn5rZk2bWKXj8YDN7x8xmmdlUM+tT47gpZvaAmd0c/1clIiIi8aR8QqRtUQFDRMLyLnBYcHsU0MnM0oHxwDzg18D/uPtIYCbw8+Dxu4DT3f1g4D7g91HHTAMeAb5091/H52WIiIhIiJRPiLQhaWEHICJt1izgYDPrDJQBnxJJPA4DXgD2BT4wM4B2wHRgKLA/MC1oTwXyoo75D+AJd49OQkRERKT1Uj4h0oaogCEioXD3cjNbDnwP+BCYCxwF7AEsA6a5+znR+5jZAcB8dx9bx2E/BI4ys/9199IWC15EREQSgvIJkbZFQ0hEJEzvAlcGv98DLgM+A2YA48xsTwAz62BmewOLgBwzGxu0p5vZflHHuxd4GXjSzFSgFRERaRuUT4i0ESpgiEiY3gP6ANPdvQAoBd5z93XARcCjZjaXSAIyzN23A6cDfzSzOUSSk29FH9Dd/0Kk++hDZqb/40RERFo/5RMibYS5e9gxiIiIiIiIiIjUS9VEEREREREREUl4KmCIiIgoRwKnAAAgAElEQVSIiIiISMJTAUNEREREREREEp4KGCIiIiIiIiKS8FTAEBEREREREZGEpwKGiIiIiIiIiCQ8FTBEREREREREJOGpgCEiIiIiIiIiCU8FDBERERERERFJeCpgiIiIiIiIiEjCUwFDRERERERERBKeChgikjTMbJCZuZmltcXnFxERkdgysyPNbFVbfX6RZKMChkgbY2Y3mNnDcXy+MWY2zcw2mNk6M3vSzPpEPX6Umb1lZkVmtjxecbUEM/uRmc00szIzu7+Wx482s4Vmti14zQNDCFNERCSmQsgtzjOzLVE/24ILDAdHxVNeY5sh8YovVsxsQI3XsCV4nb8IOzaRsKiAISJNshu9D7oCU4BBwEBgM/DvqMe3AvcBV8Uivmgh9JRYA9xM5PXUjKUH8AzwG6AbMBN4PK7RiYiIJKCm/r1290fcvVP1D3AFsBT4NGqzx6O3cfelMYo1NRbHaQx3X1HjdR4AVAFPxysGkUSjAoZIK2VmV5vZajPbbGaLgqv/E4DrgLOCKv6cYNu+ZvZC0EtisZldEnWcG8zsKTN72MyKgYvMLMXMrjGzJWa23syeMLNutcXh7q+4+5PuXuzu24C/AeOiHv/Y3R8ikng01vfNbI2Z5UVfhagj1tFmNt3MNgXb/83M2kXt42Z2mZl9ZWYbzezvZmbBY6lm9mczKzSzpcAJ9QXl7s+4+3PA+loePg2YH7wXpcANwAgzG9aE1y0iIhKaRMktajEJeNDdvRmv7brg7/1yMzsvqv1+M7vbzF42s63AUWZ2gpnNNrNiM1tpZjdEbV893HSSma0IjvmrqMczg2NuNLMvgEOaEOaFwLvuvnx3X6dIslMBQ6QVMrOhwI+AQ9y9M3AcsNzdXwX+wDdXJUYEuzwKrAL6AqcDfzCzo6MOeQrwFJANPAL8BDgVOCLYZyPw90aGdzgwvxkvD+AoYC/gWOAaM/ufemKtBP4f0AMYCxxN5EpNtBOJJBAjgDOJvF8AlwSPHQSMIvLe7K79gDnVd9x9K7AkaBcREUloiZpbBMMxDwcerPHQSUHxZL6ZXd7AYXoTyRP6ESmGTAleb7Vzgd8DnYH3ifQevTCI/QTgcjM7tcYxxwNDieQdvzWzfYL264E9gp/jgudrrAuBB5qwvUirowKGSOtUCbQH9jWzdHdf7u5LatvQzPoT+SN7tbuXuvtnwL+AC6I2m+7uz7l7lbuXAJcCv3L3Ve5eRqQ3wekNdQE1s+HAb2n+cJEb3X2ru88jMhzlnLpidfdZ7j7D3SuCKxb/IJIcRbvV3Te5+wrgLeDAoP1M4K/uvtLdNwC3NCPmTkBRjbYiIsmQiIhIokvI3ILISf177r4squ0JYB8gh8jFiN+a2Tm17RzlN+5e5u7vAC8RyQGqPe/uHwSxlrr72+4+L7g/l0ixpmZucWOQh8whcgGjurBzJvB7d9/g7iuBOxuICwAzOwzoRaToI9JmqYAh0gq5+2LgZ0T++K81s8fMrG8dm/cFNrj75qi2r4lchai2ssY+A4Fng2EZm4AFRBKbXnXFZGZ7Aq8AP3X395ryemoRHc/XRF5DrbGa2d5m9l8zyw+6qf6ByFWWaPlRt7cRKTYQHLfmc+2uLUBWjbYsInOCiIiIJLREzC0Cu/RKcPcv3H2Nu1e6+4fAHdTfi3Jj0DMyOtb6cotDLTIZ9zozKwIuo+Vzi0nA0+6+pZHbi7RKKmCItFLu/h93H08kIXDgj9UP1dh0DdDNzKJ7AgwAVkcfrsY+K4GJ7p4d9ZPh7qupRdC983XgpmC+i+bqXyPWNfXEejewENjL3bOIjNO1Rj5PXi3Ptbvm883VF8ysI5Huo80dTiMiIhIXiZRbAJjZOCIFgYZ6JTj1/+3vGvxdjo61vtziP8ALQH937wLc08DxozU5tzCzTOAMNHxERAUMkdbIzIaa2bfNrD1QCpQQuYoBUAAMMrMUgKD74ofALWaWEQzzuJjIeNS63AP8PihMYGY5ZnZKHbH0A94E/u7u99TyeIqZZQDpkbuWET3JZh1+Y2YdzGw/4HvUv5pHZ6AY2BJMmNnQONhoTwA/MbNcM+sKXFPfxmaWFryWVCA1eC3VXV+fBfY3s+8G2/wWmOvuC5sQj4iISCgSKbeIUt0rYafejGZ2ipl1tYjRRObXeL6BY91oZu2CoRonAk/Ws21nIj1MSoPjn9vAsaM9AVwbxJcL/LgR+3wH2ERkmKtIm6YChkjr1B64FSgk0oWxJ5GeB/DNH+T1Zla93Ng5RJY5XUPkRPt6d59Wz/HvIHLl4TUz2wzMAA6tY9sfAEOA6y1qHfOoxw8nkgS9TOQqRAnwWgOv7x1gMfAG8Gd3r2/7K4kkFpuBf9K0pUv/CUwlMnb1UyLLoNbn10TivwY4P7j9awB3Xwd8l8gkYBuJvF9nNyEWERGRMCVSbkFwMeBMau+VcDaRPGEzkck9/+ju9fVeyCfyt3kNkSLLZQ1cYLgC+F0Q52+JFCUa60Yiw0aWEcl3GtMztdmrrIi0FqbvgYiIiIiIiIgkOvXAEBEREREREZGEpwKGiIiIiIiIiCQ8FTBEREREREREJOGpgCEiIiIiIiIiCU8FDBERERERERFJeGlhB5AoevTo4YMGDQo7DBERiaGl67YCMCSnY8iRtB2zZs0qdPecsOMIg3IJEZHWR7lEOOrKJ1TACAwaNIiZM2eGHYaIiMTQWf+YDsDjl44NOZK2w8y+DjuGsCiXEBFpfZRLhKOufEJDSEREREREREQk4amAISIiIiIiIiIJT0NIRESk1erTJSPsEERERCSJKZdILCpg1KO8vJxVq1ZRWloadihJLyMjg9zcXNLT08MORUTakL+efVDYIYiIiEgSqy2X0Hli7DT1PFEFjHqsWrWKzp07M2jQIMws7HCSlruzfv16Vq1axeDBg8MOR0REREREZLfpPDE2duc8UXNg1KO0tJTu3bvrQ9lMZkb37t1VoRSRuLvxxfnc+OL8sMMQERGRJFVbLqHzxNjYnfNE9cBogD6UsaH3UUTC8MWa4rBDEBERkSRWVy6h85vYaOr7qB4Yrcjbb7/Nhx9+2KxjdOrUKUbRiIiIiIiISNha03miChitSCw+mCIiIiIiItJ6tKbzRBUwYui52asZd+ubDL7mJcbd+ibPzV4dk+OeeuqpHHzwwey3335MmTIFgFdffZWRI0cyYsQIjj76aJYvX84999zD7bffzoEHHsh7773HRRddxFNPPbXjONVVsy1btnD00UczcuRIDjjgAJ5//vmYxCki0pa01P/5IhIufbdFJNZ0nhg7mgMjRp6bvZprn5lHSXklAKs3lXDtM/MAOPWgfs069n333Ue3bt0oKSnhkEMO4ZRTTuGSSy7h3XffZfDgwWzYsIFu3bpx2WWX0alTJ6688koA7r333lqPl5GRwbPPPktWVhaFhYWMGTOGk08+WeO4RKTVGZLTsUWO25L/54tIePTdFpGamptL6DwxtuJewDCzocDjUU1DgN8CDwbtg4DlwJnuvtEi79YdwPHANuAid/80ONYk4NfBcW529weC9oOB+4FM4GXgp+7uzYn7xhfn1zsZ3OwVm9heWbVTW0l5Jb98ai6Pfryi1n327ZvF9Sft1+Bz33nnnTz77LMArFy5kilTpnD44YfvWGqmW7dujX0ZQGS5muuuu453332XlJQUVq9eTUFBAb17927ScUREEt0tpw1vkePeNnXRjkSkWkl5JbdNXaSTHJEkpu+2iNTUUC6h88T4ivsQEndf5O4HuvuBwMFEihLPAtcAb7j7XsAbwX2AicBewc9k4G4AM+sGXA8cCowGrjezrsE+dwfbVu83oaVfV80PZUPtjfX222/z+uuvM336dObMmcNBBx3EiBEjGlUFS0tLo6oq8vzuzvbt2wF45JFHWLduHbNmzeKzzz6jV69eWuJURKQJ1mwqaVK7iCQHfbdFJNZ0nhhbYQ8hORpY4u5fm9kpwJFB+wPA28DVwCnAg0EPihlmlm1mfYJtp7n7BgAzmwZMMLO3gSx3nx60PwicCrzSnEAbqoCNu/VNVtfyx61fdiaPXzp2t5+3qKiIrl270qFDBxYuXMiMGTMoKyvjnXfeYdmyZTt1DercuTPFxd9U/wYNGsSsWbM488wzef755ykvL99xzJ49e5Kens5bb73F119/vdvxiYgksmufmQvEvidG3+zMWv/P75udGdPnEZH40ndbRGpqKJfQeWJ8hT2J59nAo8HtXu6eBxD87hm09wNWRu2zKmirr31VLe0t6qrjhpKZnrpTW2Z6KlcdN7RZx50wYQIVFRUMHz6c3/zmN4wZM4acnBymTJnCaaedxogRIzjrrLMAOOmkk3j22Wd3TM5yySWX8M477zB69Gg++ugjOnaMjN8677zzmDlzJqNGjeKRRx5h2LBhzYpRRCRRLV23laXrtsb8uFcdN5S0lJ2vcMTi/3wRCVdL5XMikryam0voPDG2QuuBYWbtgJOBaxvatJY234322mKYTGSoCQMGDGggjPpVj4u8beoi1mwqoW92JlcdN7TZ4yXbt2/PK6/U3nlk4sSJO93fe++9mTt37k5tM2bM2HH7lltuAaBHjx5Mnz691mNu2bKlOeGKiLQJpx7Uj8c/WcH0pRuAyFWUWPyfLyLhqv4O/+KJOVS606FdKn/4zgH6bovIbtN5YmyFOYRkIvCpuxcE9wvMrI+75wVDRNYG7auA/lH75QJrgvYja7S/HbTn1rL9Ltx9CjAFYNSoUc2a5BMiH079gRMRaRsygqsp+/XN4qWfHBZyNCISKyeP6MuVT84Bh/37dlFuJyLNpvPE2AlzCMk5fDN8BOAFYFJwexLwfFT7hRYxBigKhphMBY41s67B5J3HAlODxzab2ZhgBZMLo44lIiISE3lFkUmtCooTa3IrEWmewq1lVFQ5aSnG0sLEuOIoIiIRofTAMLMOwDHApVHNtwJPmNnFwArgjKD9ZSJLqC4msmLJ9wDcfYOZ3QR8Emz3u+oJPYHL+WYZ1Vdo5gSeIiKSnPbtm9Vix64uXBRu2c72iirapYU9rZSIxEJ+UJw8sH82M7/eSFFJOV0y00OOSkTC0pK5hDRdKAUMd98GdK/Rtp7IqiQ1t3Xgh3Uc5z7gvlraZwL7xyRYERFJWo1ZQ313lJZXsnFbOf2CFQvWbi4lt2uHFnkuqZ2Z3QecCKx19/1rPHYlcBuQ4+6FQY/MO4hcENkGXOTunwbbTgJ+Hex6s7s/ELQfzDcXQ14GfhrkJNLKVRcwvrVHd2Z+vZGl67Zw0ICuIUclImFpqVxCdo8uF4mIiDRRde+LAwdk73Rf4up+YELNRjPrT6SX54qo5onAXsHPZODuYNtuwPXAocBo4PpgWCrBNpOj9tvluaR1yg++z9/aswcAywpjv5KRiIjsHhUwRESk1frZY7P52WOzY37cHV3Mc6sLGGUxfw6pn7u/C2yo5aHbgV+y8wpkpwAPesQMIDuYMPw4YJq7b3D3jcA0YELwWJa7Tw96XTwInNqSr0cSR15RKWkpxkEDsklNsRZZillEkkdL5RKye1TAaGM6deoEwJo1azj99NPr3favf/0r27Zta9Lx3377bU488cTdjk9EJJbyikp3TLYZS/k1emDkt8BzSNOZ2cnAanefU+OhfsDKqPurgrb62lfV0i5tQH5RKb2yMmiflsqAbh00kadIG9dSuUSiSZbzRBUwWoHKysom79O3b1+eeuqperfZnQ+miEhbUF2wGNq7M+3SUjSEJAEEE4T/CvhtbQ/X0ua70V7b8042s5lmNnPdunWNDVcSWH5RKb27ZAAwpEdH9cAQkaTVGs8TVcCItbw8OOIIyM+PyeGWL1/OsGHDmDRpEsOHD+f0009n27ZtDBo0iN/97neMHz+eJ598kiVLljBhwgQOPvhgDjvsMBYuXAjAsmXLGDt2LIcccgi/+c1vdjru/vtH5jyrrKzkyiuv5IADDmD48OHcdddd3HnnnaxZs4ajjjqKo446CoDXXnuNsWPHMnLkSM444wy2bIlckXj11VcZNmwY48eP55lnnonJ6xYRSWT5xaV0ap9GVkY6vbLa7+iRIaHaAxgMzDGz5UAu8KmZ9SbSg6J/1La5wJoG2nNrad+Fu09x91HuPionJydGL0XClF8cVcDI6ciywq1UVWn+VhFpJp0nxuR1q4ARazfdBO+/H/kdI4sWLWLy5MnMnTuXrKws/u///g+AjIwM3n//fc4++2wmT57MXXfdxaxZs/jzn//MFVdcAcBPf/pTLr/8cj755BN69+5d6/GnTJnCsmXLmD17NnPnzuW8887jJz/5CX379uWtt97irbfeorCwkJtvvpnXX3+dTz/9lFGjRvGXv/yF0tJSLrnkEl588UXee+898mP0hRQRSWSRLubtAeidlaEhJAnA3ee5e093H+Tug4gUIUa6ez7wAnChRYwBitw9D5gKHGtmXYPJO48FpgaPbTazMcEKJhcCz4fywiSu3J28ohL6ZEUKGIN7dKKsooo1RSUhRyYiSU/niTF5zaEso5q0jjxy17Yzz4QrroBt2+Doo+Hjj6GqCu65B2bPhsmT4aKLoLAQao4levvtRj1t//79GTduHADnn38+d955JwBnnXUWAFu2bOHDDz/kjDPO2LFPWVlkQrkPPviAp59+GoALLriAq6++epfjv/7661x22WWkpUU+Dt26ddtlmxkzZvDFF1/siGP79u2MHTuWhQsXMnjwYPbaa68d8U2ZMqVRr0tEpKWNHNgySx9GX6HtlZXB56uLWuR5pG5m9ihwJNDDzFYB17v7vXVs/jKRJVQXE1lG9XsA7r7BzG4CPgm2+527V08MejnfLKP6SvAjrVxRSTml5VU79cAAWLpuq5ZKFmmjGpVL6DwxbueJKmDE0tdfQ/US8e6R+zEQufiz6/2OHSN/VKuqqsjOzuazzz5r1P41uXujtjnmmGN49NFHd2r/7LPPGtxXRCQsV08Y1iLHzS8q5Vt7RJZY7JWVwesLChr1f6nEjruf08Djg6JuO/DDOra7D7ivlvaZwP7Ni1KSTfVwsF0LGFs4fG8NERJpi2KSS+g8MWY0hKQp3n5715+gCw5FRbBx484fzI0bYUKwbHyPHrvu20grVqxg+vTpADz66KOMHz9+p8ezsrIYPHgwTz75ZPDUzpw5kQnYx40bx2OPPQbAI488Uuvxjz32WO655x4qKioA2LAhcvGpc+fObN68GYAxY8bwwQcfsHjxYgC2bdvGl19+ybBhw1i2bBlLlizZEZ+ISGtWWeWs3VxG7y7fDCEpLa+iuKQi5MhEpLmqVxroExQwcjq1p3P7NJYWaiJPEamHzhPjdp6oAkas3HRTpEtQtMrKmIxx2meffXjggQcYPnw4GzZs4PLLL99lm0ceeYR7772XESNGsN9++/H885GhunfccQd///vfOeSQQygqqr2L8w9+8AMGDBjA8OHDGTFiBP/5z38AmDx5MhMnTuSoo44iJyeH+++/n3POOYfhw4czZswYFi5cSEZGBlOmTOGEE05g/PjxDBw4sNmvV0QkVi57aBaXPTQrpsdcv6WMyiqnd5dMAHoFJzoFmzUPhkiyq57Ppvr7bWYMDibyFJG2qdm5hM4TY3qeaO6aVRlg1KhRPnPmzJ3aFixYwD777NO4Axx0ENTWNefAAyNjnHbT8uXLOfHEE/n88893+xiJoknvp4hIDJz1j8hViccvHRuzY85ZuYlT/v4B/7xwFMfs24uPl23gzH9M58Hvj1YXc8DMZrn7qLDjCENtuYQkl9unfcmdb37FlzdPJD01cp3vZ4/N5pPlG/ngmm+HHJ2IhKG2XELnibFV2/tZVz6hOTBipRkfPhERSR47xsgHqxRU/9ZSqiLJL7+olB6d2u8oXgAMyenEc5+toWR7JZntUkOMTkSSks4TY0pDSBLcoEGDWkVVTUSktSioMclfz2A51QItpSqS9PKKS3fMf1GteiJPDSMRkUTSVs8TVcAQERFpgryiUtJTje4d2wGQkZ5K1w7p6oEh0grkF5Xs6FVVbXAPFTBERBKFhpA0QMvixYbmWhGRMIzbs0fMj1lQVErPzhmkpHzzt6FXVsaOnhkikrzyi0oZM6T7Tm3VBYyl67aEEZKIhKyuXELnibHR1PNEFTDqkZGRwfr16+nevbs+nM3g7qxfv56MjIyGNxYRiaGfHL1XzI+ZV1S6Y/hItV5ZGeqBIZLktpZVUFxascv3u0O7NPp2ydBSqiJtVG25hM4TY2N3zhNVwKhHbm4uq1atYt26dWGHkvQyMjLIzc0NOwwRkWYrKC5lnz5ZO7X1zsrgi7zikCISkVioLkLWnAMDIhN5qgeGiFTTeWLsNPU8UQWMeqSnpzN48OCwwxARkd006b6PAXjg+6Njcjx3J7+4lKOG9dypvVeXDAq3lFFeWbXT6gUikjzyi6pXGMrc5bHBPTry3Ger1WVcpA2qLZfQeWJ4lGWJiEirVVpeSWl5ZcyOV1xawbbtlbtM8tc7KwN3WLe5LGbPJSLxtaOAUWsPjI5sLq2gcMv2eIclIiGLdS4hzaMChoiISCNVT9TZq8YJTu8ukaVUNQ+GSPKq/v7WLFBCZAgJaCJPEZGwqYAhIiLSSNVXaGuOke8VnPAUFKmAIZKs8opKyO6QTma71F0eG1K9Eokm8hQRCZUKGCIiIo30zRj5XYeQgHpgiCSz/KLSWntfAPTLzqRdWgrLVMAQEQmVJvEUEZFW6+h9eja8URNUFyh6ZrXfqb1rh3akp5oKGCJJLL941yWSq6WkGIO7d9QQEpE2KNa5hDSPChgiItJqTT58j5geL6+olO4d29E+becu5ikpRs/OGRpCIpLE8otKOaBflzofH5LTkUX5m+MYkYgkgljnEtI8GkIiIiLSSAXFpTvmu6ipd5cMCoq1ColIMiqrqKRwy/Zal1CtNiSnIys2bKO8siqOkYmISDQVMEREpNU66x/TOesf02N2vPyi0l0m8KzWOytjxyolIpJc1gbFx7q+3wBDenSiospZuWFbvMISkQQQ61xCmkcFDBERkUbKLy7dZQnVar2yMsgvLsXd4xyViDRXfh1LJEcbnBOsRLJOE3mKiIRFBQwREZFGKKuoZMPW7XWuUtC7S3u2ba9kc1lFnCMTkebKq2OJ5Gh79OgEwNJCTeQpIhIWFTBEREQaobqLeV2rFFTPjaGJPEWST35RCVD39xugS4d0undspx4YIiIhUgFDRESkEaqv0NbZAyNo11KqIsknr6iUju1S6dy+/gX6huR0ZGmhChgiImHRMqoiItJqnTi8T8yOVV2YaKgHRr56YIgknYJgfhszq3e7wT068ubCdXGKSkQSQSxzCWk+FTBERKTVumDsoJgdq6Eu5tXtWolEJPnk1bPCULQhOZ14YuYqikvLycpIj0NkIhK2WOYS0nyhDCExs2wze8rMFprZAjMba2bdzGyamX0V/O4abGtmdqeZLTazuWY2Muo4k4LtvzKzSVHtB5vZvGCfO62hcrqIiLRKJdsrKdleGZNj5ReV0aGeLuYZ6al0yUynIJgrQ0SSR35RKb2zMhvcbkgPrUQi0tbEMpeQ5gtrDow7gFfdfRgwAlgAXAO84e57AW8E9wEmAnsFP5OBuwHMrBtwPXAoMBq4vrroEWwzOWq/CXF4TSIikmAu+vfHXPTvj2NyrILiUnpn1d/FvHewlKqIJI/KKmft5rJG98AAWLpOK5GItBWxzCWk+eJewDCzLOBw4F4Ad9/u7puAU4AHgs0eAE4Nbp8CPOgRM4BsM+sDHAdMc/cN7r4RmAZMCB7Lcvfp7u7Ag1HHEhER2S15RSX1rlAA0KtLhoaQiCSZwi1lVFY5vRpRwBjQrQOpKcYyTeQpIhKKMHpgDAHWAf82s9lm9i8z6wj0cvc8gOB3z2D7fsDKqP1XBW31ta+qpV1ERGS3FRSX1bkCSbXeWe01iadIkqleYahPA99vgHZpKfTvmqkhJCIiIQmjgJEGjATudveDgK18M1ykNrX11fXdaN/1wGaTzWymmc1ct04zSouISO2qqjwyhKSBK7S9szIo3FJGRWVVnCITkeZqaILemobkdGKJhpCIiIQijALGKmCVu38U3H+KSEGjIBj+QfB7bdT2/aP2zwXWNNCeW0v7Ltx9iruPcvdROTk5zXpRIiLSehVuLaOiyhs1hKTKYd0WTeQpkix29MBobAGjR0eWr99KVVWt18dERKQFxb2A4e75wEozGxo0HQ18AbwAVK8kMgl4Prj9AnBhsBrJGKAoGGIyFTjWzLoGk3ceC0wNHttsZmOC1UcujDqWiIi0IacfnMvpB+c2vGEDCooiBYleDXQx79U58riGkbQ8M7vPzNaa2edRbbcFK5zNNbNnzSw76rFrg9XJFpnZcVHtE4K2xWZ2TVT7YDP7KFjp7HEzaxe/VyfxlF9cSrvUFLp1bNw/8eCcjpSWV5Gn+W5E2oRY5RISG2GtQvJj4BEzmwscCPwBuBU4xsy+Ao4J7gO8DCwFFgP/BK4AcPcNwE3AJ8HP74I2gMuBfwX7LAFeicNrEhGRBHPGqP6cMap/wxs2IC/oYt7QFdrqHhqayDMu7mfXVcamAfu7+3DgS+BaADPbFzgb2C/Y5//MLNXMUoG/E1nxbF/gnGBbgD8Ctwero20ELm7ZlyNhyS8qpVeX9vWuMBRtSA+tRCLSlsQql5DYqH0x+xbm7p8Bo2p56OhatnXgh3Uc5z7gvlraZwL7NzNMERFJchu2bgdo9JXVulQXJBqaxLO6h0ZBsYaQtDR3f9fMBtVoey3q7gzg9OD2KcBj7l4GLDOzxUSWYAdY7O5LAczsMeAUM1sAfBs4N9jmAeAGgqXcpXXJKyqlT1Zmo7ffI6cjAEvXbeWwvTQEWaS1i1UuIbERVg8MERGRFnf5w7O4/OFZzT5OfnEpqSlG907t692ue8d2pGeR3gMAACAASURBVKca+eqBkQi+zzc9MJu6oll3YJO7V9Rol1Yov6jhCXqj5XRuT6f2aeqBIdJGxCqXkNhQAUNERKQBeUWl9OrcntSU+ruYp6QYPTtnUKA5MEJlZr8CKoBHqptq2UwrmgnuTn4jVhiKZmYM7tGRpYVaSlVEJN5UwBAREWlAQXEpvRp5gtMrq716YITIzCYBJwLnBcNQoekrmhUC2WaWVqN9F1rRLLlt3FbO9oqqBoeH1TQkpyNL16mAISISbypgiIiINCC/qLTRSyz27pKhAkZIzGwCcDVwsrtvi3roBeBsM2tvZoOBvYCPiUwCvlew4kg7IhN9vhAUPt7imzk0oldHk1aksRP01jSkRyfWFJVQWl7ZEmGJiEgdVMAQERFpQH5RaYNLqFbrlaUhJPFgZo8C04GhZrbKzC4G/gZ0BqaZ2Wdmdg+Au88HniCybPurwA/dvTKY4+JHRJZmXwA8EWwLkULIz4MJP7sD98bx5UmcVC953JQhJBDpgeEOyzSMREQkrkJZhURERCQezh8zsNnH2FxaztbtlY3uYt4rK4Ot2yvZXFpO54z0Zj+/1M7dz6mluc4ig7v/Hvh9Le0vE1myvWb7Ur5ZqURaqereUk0tYAzuEVmJZFnhVvbpkxXzuEQkccQil5DYUQFDRERarZNG9G32MZp6hbb3jqVUS1XAEElw+UWlpBjkNLDCUE1DdiylqpVIRFq7WOQSEjsaQiIiIq3Wmk0lrNlU0qxj7LhC24QeGAAFxWXNel4RaXl5RaX07JxBWmrTUuIO7dLo0yVDE3mKtAGxyCUkdtQDQ0REWq3/9/hnADx+6djdPkaTe2AE2+VrHgyRhJdf1LQlVKMNyenIEs2BIdLqxSKXkNhRDwwREZF6VBciGjuJZ3VPDa1EIpL48otLm7yEarXBPTqybN0WvlmtV0REWpoKGCIiIvXILy6la4d0MtJTG7V9ZrtUsjLSKFABQyThNasHRo9OFJdWsH7r9hhHJSIidVEBQ0REpB4FxaX07pLZpH16d8nQEBKRBLe5tJwtZRX0acYQEkDzYIiIxJEKGCIiIvXIKyqld1bTVijolZWhHhgiCa6p89vUtEdOJ0ArkYiIxJMm8RQRkVbrksOGNPsYBcWlDM/t0qR9emVl8GXB5mY/t4i0nKauMFRT3+xM2qWlsEwTeYq0arHIJSR2VMAQEZFW63/27dWs/csqKincsp3eWU0cQpKVwbrNZVRWOakp1qwYRKRl5AU9MPo0cYhYtdQUY1D3DizREBKRVq25uYTEloaQiIhIq7Vk3RaWNKN799riMgB6d2niEJIuGVQ5FG4p2+3nFpGWVT2EpGcTh4hFG9KjE0sLNYREpDVrbi4hsaUChoiItFrXPTOP656Zt9v7V89j0dglVKvtWEpVE3mKJKy8olK6d2zX6BWGajMkpyMr1m+jvLIqhpHJ/2fv3uPbvutD/78+km3JtiRfZStxmsSX1OklpaFteqFcC7Rc23V0lLOVHgYrDDbG2Cmjv7OzHthhhZX9GLBxKbTcD5exrrRdIetKoUAvaUsgTkMT27m0dizZjmNJtiVZlj7nD32/jpPYsSx9pe9X9vv5ePhh66uvpI+b2v7o/X1fhHCSYvcSwloSwBBCCCGWUGiK+XwAQxp5CuFYkVhyxcHJU3UFfcxlNUPHExatSgghxJlIAEMIIYRYQqTAJn/tRsmJTCIRwrlGosmCR6iaOlvNUaqSXi6EEOUgAQwhhBBiCeFoEm+1i0Dtynpet9Z7cLuUlJAI4WDhaKLgEaqm7qAZwJBGnkIIUQ4SwBBCCCGWMBJLsq6hFqVWNknE5VK0+T1SQiKEQyXTGY7PpIvOwGisq6G5vkYaeQohRJnIGFUhhBCr1p+/ZktRj49Ek7QXOKGgPeCVEhIhHKrQBr2L6WqtlwwMIVaxYvcSwloSwBBCCLFqXbmltajHj0ST7OhsLuixoYCXAamLF8KRCm3Qu5jO1np+dmCs6OcRQjhTsXsJYS0pIRFCCLFqPXc0ynNHowU9NpvVjMYLn1IQavASkR4YQjiS2Z+m2B4YkJtEMhZPEU+mi34uIYTzFLOXENaTAIYQQohV6+MP7OPjD+wr6LETM7OkM5pQESUk8dQc06m5gh4vhCgdsz+NNQEMaeQpxGpWzF5CWE8CGEIIIcQiTlyhLSzFPGSMUpVGnkI4TziaxO+pwucpvpp6fhKJNPIUQoiSkwCGEEIIsYhiU8zN0hMpIxHCeUYsGKFqOqu5DpeCQ5KBIYQQJScBDCGEEGIR8ynmhfbAMB4nGRhCOE84mrQsgOGpcnNWcx2D4xLAEEKIUpMAhhBCCLGIcDSJ26UI+gvvgQESwBDCicKxJOssCmCAjFIVQohykTGqQgghVq2PXNNb8GPDsSRBnwe3SxX0+HpPFX5PlZSQCOEw6UyW0Xiq4OyqxXQFfTxx8BjZrMZV4O8MIYQzFbOXENaTAIYQYs27b/cwd+7cz9HJBOsba7n16l6u295h97KEBS7a1FzwY61IMW9v8BKJpYp6DiGEtcbiKbQuvEHvYjpb60mms4RjSdY3Wve8Qgj7FbOXENazpYREKXVYKdWnlPqNUuoZ41izUuphpVS/8bnJOK6UUp9TSg0opfYopV664HluNs7vV0rdvOD4RcbzDxiPlVC4EGJR9+0e5rZ7+xieTKCB4ckEt93bx327h+1emrDAs0cmePbIREGPDceSRV+hDQW8UkIihMOMGFlRlpaQyChVIVatYvYSwnp29sB4tdb6Qq31xcbtjwKPaK23AI8YtwHeAGwxPm4Bvgi5gAdwO3ApsAO43Qx6GOfcsuBx15T+2xFCVKI7d+4nkc6cdCyRznDnzv02rUhY6R9+sp9/+Elh/5YRKzIwAl4iEsAQwlHMn0mrmngCdAd9gIxSFWI1KmYvIaznpCae1wLfML7+BnDdguPf1DlPAo1KqXXA1cDDWusJrfVx4GHgGuO+gNb6Ca21Br654LmEEOIkRycTKzou1oap1Bzx1FzRb3BCDR5G4ykyWW3RyoQQxTIzMKzsgdHm91Bf45YMDCGEKDG7Ahga+E+l1LNKqVuMY+1a6xEA43ObcbwDeHHBY4eMY2c6PrTIcSGEOM1StcpSw7y2hS16gxMKeMlkNcempA+GEE4RjibwVLlorKu27DmVUnQG6zkoo1SFEKKk7ApgvExr/VJy5SEfUEq94gznLta/Qhdw/PQnVuoWpdQzSqlnxsbGlluzEGIVuvXqXqpO6RhfW+3m1qul4/RaZqaYtxcZwJBRqkI4z0g0N0LV6hZpXa0+Do5JCYkQQpSSLQEMrfVR4/Mo8O/kelhEjPIPjM+jxulDwFkLHr4BOLrM8Q2LHF9sHXdprS/WWl8cDAaL/baEEBXouu0dbA3552+HAl7uuH6bTCFZ46xq8jcfwJBRqpZTSt2jlBpVSu1dcEwagotlRWLF97dZTFewnuHJBMlT+ioJIYSwTtkDGEqpeqWU3/waeD2wF7gfMDcONwM/Mr6+H3insfm4DIgaJSY7gdcrpZqMDcrrgZ3GfXGl1GXGZuOdC55LCCFOMz2bobm+BoBP3/ASCV6sIn/7lnP527ecu+LHWdXkz3y8NPIsia9zepNuaQguljUSLX7C0GK6gj60hsPHpIxEiNWk0L2EKA07MjDagV8qpX4L7AL+Q2v9E+CTwOuUUv3A64zbAA8BB4EB4CvA+wG01hPA3wFPGx8fN44B/CnwVeMxg8CPy/B9CSEqUDKd4cixaa45PwTA8+GYzSsSVjpvfQPnrW9Y8eNGogka66rxVruLev1Wnwe3SxGJSQ8Mq2mtHwNOnWsnDcHFGWWz2sjAsL7PUVdrbpTqIWnkKcSqUuheQpRGVblfUGt9EHjJIsePAVctclwDH1jiue4B7lnk+DPA+UUvVgix6h0cmyar4bKuFv7zuTD7w3G7lyQs9Mv+cQCu3NK6oseFoylLrtC6XYqgzyM9MMrnpIbgSilpCC5Ocmx6lnRGF10etphOI4AhjTyFWF0K3UuI0ih7AEMIIZykfzQXsDi73UdvyM/+iAQwVpPP/7QfWPmmIxJLFt3A09Te4JUSEvuVtCE4uVITNm7cWOj6RJlYVR62mHpPFaGAl0Fp5CnEqlLoXkKUhl1TSIQQwhEGRqdwqdyVs972AAcicbLZRd+niDXEnFJghVDAI008y0cagoszGrFoRPJSuoL1HJQSEiGEKBkJYAgh1rQDkTibW+rxVLnZGvKTTGd5YWLG7mUJG6UzWY5NpyzLwAgFvFJCUj7SEFycUTiaAIqfMLSUztZ6Do5NkauAFkIIYTUpIREiT/ftHubOnfs5OplgfWMtt17dK9MqVoH+0Sm2tPsA6DXGqT4fjrPZqGUWa89oPIXW1qWYtwW8xJNzzMzOUVcjf3atopT6LvAqoFUpNURumsgngR8opd4NvADcYJz+EPBGcs29Z4B3Qa4huFLKbAgOpzcE/zpQS64ZuDQEXwVGokmqXIoWn6ckz98V9BFLzjExPVuy1xBCiLVMdlJC5OG+3cPcdm8fCWO2+/Bkgtvu7QOQIEYFS81lOHJshjeevw6As9v9KAX7w/H5qSRi7TGv0FoVwDBT1cPRJF1BnyXPKUBr/Y4l7pKG4GJJYaO/jdu1WJuT4nUFTzTylACGEEJYTwIYQuThzp3754MXpkQ6w50790sAo4IdHp8hk9XzGRi1NW42NdexPyKjVFeLv79+24ofE47mRp5aVSNvBkLCMQlgCGG3cDRJe6B0gYXu1tzP+MGxKS7Z3Fyy1xFClE8hewlROhLAECIPRycTKzouKsMBY+JIT9uJN5W9IT/PyyjVVaO7gIDBiJmBYdUUEuN5RmMpS55PCFG4cDTJOesCJXv+jqZaatwuGaUqxCpSyF5ClI408RQiD+sba1d0XFSGfmMCycI/TL2hAIfHp0meknEjKtN/7YvwX/siK3pMJJbEU+Wisa7akjUszMAQQthHa81INFmSEaomt0uxqaVOJpEIsYoUspcQpSMBDCHycOvVvdRWu086Vlvt5tare21akbDCwGicjc11eBf82/a2+8nq3HhVUfm+8ouDfOUXB1f0mHAsRajBS274RPF8nip8nioZpSqEzWLJORLpTMlGqJpyo1Tlb4gQq0UhewlROhLAECIP123v4OPXnjd/u9qtuOP6bdL/osL1R6boafOfdGzhJBKxNoWjCcvf4LQHPEQkA0MIW5lBxFJmYEBuEskLEzPMZbIlfR0hhFiLJIAhRJ66jT4JoYCX2mo311643uYViWLMzmU5ND4938DTtLmljpoqF/vD0shzrQrHrE8xDzV4pYRECJuZ/W3WlTqA0VpPOqMZOi59soQQwmoSwBAiT31DUQCuvXA9seQc41OzNq9IFOPIsWnmspqzTwlgVLldbGnzSQbGGqW1JhJNWZ+B4fcSkRISIWxVvgwMc5SqlJEIIYTVJIAhRJ72DEVp9Xm4oqcVkB4Jla7f+PfbckoJCeTKSPZLAGNNmpieZTaTtfwNTnuDl9F4imxWW/q8Qoj8mVlQbf5SZ2CYo1SlkacQQlhNxqgKkae+4Uku2NAwP3JzcGyKy7tbbF6VKFR/ZAqlFh+NtTXk595fD3N8epam+hobVies8pm3X7ii8803OFZnYIQCXuaymvHpVMnfPAkhFheOJmn1eaipKu31u6b6GprqqhmUAIYQq8JK9xKitCQDQ4g8zMzOMTA6xbaOBtY3eKmrcUsGRoU7MBrnrKY6amvcp93XGwoA0shzNVjfWLuiccdminm71RkYRkBkNJay9HmFEPkbiSZL3v/C1BX0ySQSIVaJle4lRGlJAEOIPOw7GiOrYVtHA0opuoM+BmVjUtEGIlNsaTs9+wJyGRiANPJcBR747VEe+O3RvM83MzCsfpNjlqTIKFUh7BOOWt+gdymdrfUcGpcMDCFWg5XuJURpSQBDiDzsMRp4btvQAEB3sJ5BycCoWHOZLAfHp+hpXzyA0eb30FhXzf6IZGBUum8/eYRvP3kk7/Mj0SQuBUGfx9J1mCUpMolECPuEY0nLy8OW0hWsZzSeIp5Ml+X1hBCls9K9hCgtCWAIkYe9w1HaA575NPCeNh9Ho0mmU3M2r0wU4vCxGdIZvWgDTwClFL3t0shzLRqJJgn6PVS5rf3z2OqrwaUgIgEMIWwxMztHNJEuWwaG2chTsjCEEMJaEsAQIg97hqNs62iYv202fpQO45VpYDQXmDh1hOpCW0N+DkSm0FqmRqwlpbpCW+V2EfR7pIRECJuYP3vl6oHRbY5SlX2CEEJYSgIYQixjKjXH4NgU2zoa54+Zk0gGxuQKfSXqj+TKfxabQGLqDQWYSs0xdDxRrmUJB4jEkvOZVlZrD3ilhEQIm5gBjHJlYGxsqcOl4KBkYAghhKUkgCHEMvYdjaE1XLDhRAbGppZ63C7F4KhsTCpR/+gUHY211HuWniTdO9/IU4JUa0kppxS0B7xSQiKETUo1Inkpnio3G5rqZBKJEEJYbOnduxACgD1DkwCcv6CEpKbKxabmOhmlWqEOROJsOUP5CCwIYETivPbc9nIsS5TAF//oorzPnU7NEU/OWT5C1RQKeNl1aKIkzy2EOLORMmdgQK6Rp5SQCFH5VrKXEKUnGRhCLKNvOMq6Bi9B/8lTCbrbfAzIlZWKk5tAMs3Z7Ys38DT5PFVsaKrlecnAqGjN9TU019fkdW6pr9CGGrxEE2mS6UxJnl8IsbRwNElDbTV1NeW7dtfV6uPQ+DTZrPRSEqKSrWQvIUpPAhhCLKPvlAaepu6gjyPHpklnsjasShTqxeMJZuey831MzmRryM/+cKwMqxKl8q/PvMi/PvNiXudGSnyF1uytIY08hSi/UpaHLaUzWE8inSESl595ISrZSvYSovSKCmAopW5QSvmNr/9GKXWvUuql1ixNCPvFk2kOjk0vGsDoafORzmhemJixYWWiUP2RXEbFljwCGL0hPwfHppmdkyBVpfrhs0P88NmhvM4teQaGGcCQPhgnkb2EKIdSNuhdSnerTCIRYjVYyV5ClF6xGRj/S2sdV0pdCVwNfAP4YvHLEsIZ9g7nrr5v27BYBkZuYzIofTAqSr/x77VlmRISgLPb/cxlNYNSKrQmlLpGPtSQK0OTRp6nkb2EKDk7MjC65keuy98QIYSwSrEBDLOQ903AF7XWPwKkQEisGnuHowCLl5DMj1KVjUkl6Y/EWd/gxXeGCSSmraEAIJNI1opILEnAW1WyGnkpIVmS7CVESc3OZRmfSpW1gSdAe8BDXY2bQcnAEEIIyxQbwBhWSn0Z+APgIaWUx4LnFMIx9gxH6WispcXnOe2+gLea9oBHJpFUmP7RKXryyL6AXAf5areSRp5rRDiaLOkbHJ+niroat5SQnE72EqKkzKyncmdgKKXobK3n0LgEMIQQwirFbhD+ANgJXKO1ngSagVuLXpUQDtE3NLlo9oWpO+iTKysVJJPVDIxO5dX/AqDa7aI76JNGnmtEOJYk1FBbsudXShEKeKWE5HSylxAlZf7MlbsHBuTKSA6Oy4UOIYSwSlF5slrrGaXUKHAl0A/MGZ+FqHjRRJrDx2a44eKzljynp83Hvb8eRmuNUqqMqxOFGDo+Q2ouy9nt+QUwINfI8+lDEyVclSilr79rR97nhqNJtobyy84pVHvASySWKulrVBrZS4hSM/vbrCthgHIpXa31PLjnKMl0Bm+1u+yvL4Qo3kr2EqL0ip1Ccjvw18BtxqFq4NvFLkoIJ3juDP0vTN1BH1OpOUbj8oakEvRHclfBetryf5PaG/JzNJokmkiXalmihGpr3NTWLP+mIZ3JMjaVKtkEElOowSs9ME4hewlRauESN+g9k65gPVrDkWMysUyISpXvXkKUR7ElJL8HvBWYBtBaHwXyemeglHIrpXYrpR40bncqpZ5SSvUrpb6vlKoxjnuM2wPG/ZsXPMdtxvH9SqmrFxy/xjg2oJT6aJHfo1ij9uQRwOgxG3lKH4yKYE4g6cmzhASYvyJ/ICJ9MCrRt544zLeeOLzseWPxFFpT0hISyGVgjMaTZLO6pK9TYQreSwiRj5FokroaNwFvaRr0nklXa+7vzSEpIxGiYuW7lxDlUWwAY1ZrrQENoJSqX8Fj/wL43YLbnwI+o7XeAhwH3m0cfzdwXGvdA3zGOA+l1LnAjcB5wDXAF4ygiBv4F+ANwLnAO4xzhViRvuEoZzXX0lS/dDP8bmNEmozZrAz9kTjtAQ8NtdV5P6bXmEQijTwr04N7Rnhwz8iy55mNNc1Rp6USCnhIZzQTM7MlfZ0KU8xeQohlRWJJQgGvLaWenebIdemXJUTFyncvIcqj2ADGD4zO4Y1KqT8B/gv4ynIPUkptIDcu7avGbQW8Bvihcco3gOuMr681bmPcf5Vx/rXA97TWKa31IWAA2GF8DGitD2qtZ4HvGecKsSJ9Q9EzZl9AbkSaz1MlGRgVon90irPznEBiWt/gxe+t4oAEMFY1M8W81E3+zBR2KSM5SUF7CSHyNRJN2FI+ArnpQ+0BDwclgCGEEJYoKoChtf40uaDCvwG9wN9qrT+fx0P/CfgIkDVutwCTWus54/YQ0GF83QG8aLzeHBA1zp8/fspjljouRN4mZ2Z5YWKGbR2NZzxPKUV3m08CGBUga0wgWUn5COT+jXvb/eyXAMaqFi5Tk782I0Aik0hOKGIvIUReSj0ieTldrTKJRAghrFL0nHWt9cNa61u11v9Da/3wcucrpd4MjGqtn114eLGnXua+lR5fbC23KKWeUUo9MzY2doZVi7Vm73BubOYFG86cgQHQHayXEpIKMDyZIJHOsGUFDTxNvSE/z4dj5LLcxWoUiSWpqXLRVJd/eVEhzCahYQlgnGSle4nlKKX+Uin1nFJqr1Lqu0opr5W9tkTlyGQ1kXiKdTYGMDqD9RwalwwMIYSwQkEBDKVUXCkVW+QjrpSKLfPwlwFvVUodJlfe8RpyGRmNSimzu9IG4Kjx9RBwlvG6VUADMLHw+CmPWer4abTWd2mtL9ZaXxwMBvP63sXasGd4EoDz1y8fwOhp8xGJpYglZUqFk/WP5jIoVjJC1bQ15CeWnJM3navYSLQ8NfJBvwelICIlJMXuJc70vB3AB4GLtdbnA25yPbMs6bVV6LqEPY5NpchkdcknDJ1JV2s9kzNpJqal940QQhSroACG1tqvtQ4s8uHXWgeWeextWusNWuvN5DYGP9Va/yHwKPA247SbgR8ZX99v3Ma4/6dGs6/7gRuNKyedwBZgF/A0sMW40lJjvMb9hXyfYu3qG4qyqaWOhjyuxpqNPKW+1dlOjFBdeQBDGnlWru+/93K+/97Llz0vbDT5K7Vqt4tWn4dITEYvF7OXyEMVUGtc+KgDRrCu15aoICPzI1RLWx52Jif2CZKtKUQlyncvIcqj6BISAKVUm1Jqo/lR4NP8NfBhpdQAuR4XdxvH7wZajOMfBj4KoLV+DvgBsA/4CfABrXXG6JPxZ8BOclNOfmCcK0Te+oaXb+BpklGqlaF/dIqg30Nj3dJTZZbSazT+lD4Yq1c4mqS9TCnmoYBXsnkWYdFeAq31MPBp4AVygYso8CzW9do6dd1SjupgI/P9bWzMwDAmkciFDiGEKF5RA7GVUm8F/hFYD4wCm8gFDc7L5/Fa658BPzO+PsgiVza01knghiUe/wngE4scfwh4KJ81CHGqielZho4nuOmyTXmdv7G5jmq3kgCGw/WPTrGlgOwLgIa6atY1eCWAUYHuemwQgFte0b3kOVprwrEk15TpDU57wMvQ8ZmyvFYlKHYvscjzNZHLnugEJoF/JTda/VSF9to6+YDWdwF3AVx88cXSKMdhwtEEgK1NPDc05fYJB6UPhhAVKZ+9hCifYjMw/g64DDigte4ErgJ+VfSqhLBR33AUgG15NPCEXEr4phZp5OlkWmsGIvEVj1BdKNfIUwIYleaR343yyO9Gz3jO5Eya2blsyUeomkINHsnAOJnVe4nXAoe01mNa6zRwL3AF1vXaEhUkHEtR7VY0F5B9ZxW3S7GppV5KSISoUPnsJUT5FBvASGutjwEupZRLa/0ocKEF6xLCNnuNAMb5eZaQAPQEfQxKBoZjHY0mmZ7NFNT/wtTb7mdwdIp0Jrv8yaKizNfIlyuAEfAyOZMmmc6U5fUqgNV7iReAy5RSdUYvi6vIlZta1WtLVJBwNEF7wIvLVdoGvcvpaq2XDAwhhLBAsQGMSaWUD3gM+I5S6rPA3DKPEcLR9gxN0tlaT8Cb/zjF7rZ6jkzMMDsnb26dqD+Sy5wotIQEchkYs5ksh2UDuupEYmaTv/IEMNqMQElEsjBMlu4ltNZPkWvG+Wugj9xe5y4s6rVV6LqEPUaiSVv7X5i6gj6OHJtmToLgQghRlGIDGNcCM8BfkvvjPgi8pdhFCWGnvqH8G3iaetp8ZLKaI8fkza0Tmf1JthRZQgIyiWQ1Cpc5gGFmeoRllKrJ8r2E1vp2rfVWrfX5WuubjEkiB7XWO7TWPVrrG7TWKePcpHG7x7j/4ILn+YTWultr3au1/nExaxL2iMSStk4gMXUF60lnNMOTCbuXIoQQFc2SKSRG1+4ngMNAwbPbhbDb+FSKo9EkF+TZ/8JkjkiTPhjOdCASp9VXQ3N94TXQPW0+3C4ljTwrjLfajbfafcZzRqJJlII2v6csazIDJdIH42SylxBW01ozEk0SCpTnZ/tMulplEokQlSqfvYQon6KmkJBL93y50fH7EeAZ4O3AHxa7MCHs0FdA/ws4EcCQSSTO1D86VVT/CwBPlZvO1nrJwKgw3/jj04ZbnSYSTdLq81DttiSmvyyzWehoLFWW16sAspcQJTE5kyY1l3VIBsaJCx2v3tpm82qEECuRz15ClE+xuzWltZ4Brgc+r7X+PeDc4pclhD36hqIoBeetD6zocfWeKtY3eCWAi2qM4AAAIABJREFU4UC5CSRTbGkrvHzE1Bvysz8iF4ZXm5FYsmwNPAEC3ipqq92SgXGC7CVESZgNep3QA6O5vobGumpp5CmEEEUqOoChlLqc3FWS/zCOFZvVIYRt+oajdLXW419BA09Td5uPQUkNdZxILEU8NcfZ7cVlYABsbffz4kSCqZT0Kq4Un3ukn8890n/GcyLRZNn6XwAopQg1eCWAcYLsJURJlLtB73K6WmWUqhCVKJ+9hCifYgMYfwHcBvy71vo5pVQXuTFlQlSkQhp4mrqDPgbHpshmtcWrEsU4YEwg6bEoA2Phcwrn+9XAOL8aGD/jOeEyZ2AAtAc8RKSJp0n2EqIkyj0ieTmdrT4OSQaGEBUnn72EKJ+iAhha68e01m/VWn/KuH1Qa/1Ba5YmRHmNxpOEY0m2bWgs6PHdbT5mZjNyVdVh+ucnkFiQgRHKlRYdkD4Yq0ZiNkM0kS77FdpQQDIwTLKXEKUSjiZwKQiWqUHvcrqC9URiKcniE0KIIpSnY5kQFWCv0cBzpRNITD3SyNORBkbjNNVV01LEBBLThqZa6mrc0shzFZkfoVr2DAwvo7EUWkvGlhClMhJNEvSXr0HvcrqDuUkkh6TcVAghCuaM3+hCOMAeo4HnuetW1sDTZE65kACGsxyITLGl3Y9SqujncrkUZ7f7ZZTqKhKO2lMj3x7wMpvJMjE9W9bXFWItCceSjphAYjInkRwcl32CEEIUqqgAhlLqZfkcE6IS9A1F6Qn6qPcU1juu1VdDwFvFoDTocgytNf2ROFuKHKG60NaQn/2RuFw5rxBNdTU01S2dfROOJYDyBzDM15MyEtlLiNIJR5OEAs4oHwHY2FyHUnBQMjCEqCjL7SVEeRWbgfH5PI8J4Xh7hqNsK7B8BHKTBXrafJKB4SBj8RSx5JylAYzekJ+J6VnGplKWPaconS/ddBFfuumiJe8PR3P/jnaUkACMxuT/I2QvIUokHE2yzkEZGN5qNxuaamWUqhAVZrm9hCivgi41G+POrgCCSqkPL7grALitWJgQ5RSJJRmLpwqeQGLqDvp4dP+YRasSxTrRwLP4CSQmcxLJ/nCcNr8zOtuLwoWjCfyeqoIzrwolGRiylxClNZWaI56ac8wIVVNXq09GqQohRBEKzcCoAXzkAiD+BR8x4G3WLE2I8tkzVFwDT1NPm4/xqRTRmbQVyxJFMsedWjGBxGROIpE+GJXhUz95nk/95Pkl78/VyJf/DU6b34NSJ3pwrFGylxAlY/5srXNaACNYz6HxaSlDFKKCLLeXEOVV0CUnrfXPgZ8rpb6utT5i8ZqEKLu+oUlcCs5dV3wAA2BgLM5Fm5qtWJooQv/oFA211QR91tVAN9fXEPR7ZBJJhfj1keNnvD8cS9kSwKh2u2ip9xBZwxkYspcQpWQGMNrLXB62nK7WemZmM0Rs+t0jhFi55fYSorwKLSH5J631h4B/VkqdFkLWWr+16JUJUUZ9w1G2tPmprSkua7nb6DA+ODotAQwHGIhMsaXNZ8kEkoV6ZRLJqhGOJtjSFrTltUMNnrVeQiJ7CVEyI9Fcg17nZWAYk0jGpiSAIYQQBSi06PdbxudPW7UQIeyitaZvOMqretuKfq6zmuuocbsYkPpW22mtOTAa5w3nr7P8uXtDfr795BEyWY3bZW1wRJTPXCbLWDxl2xucdr+X4cmELa/tELKXECXj2AyMYD0Ag+PTXNHTavNqhBCi8hRaQvKs8fnn1i5HiPIbiSYZn5otuv8FgNul6GytZ1AmkdhufGqWyZm0pRNITL0hP6m5LEeOTc9fTROVZ2wqRVbb9wanvcHLr19Yu2mpspcQpRSOJWmur8Fb7ax+sKGAl9pqtzTyFEKIAhXVdt2Y0/6/gU3GcylAa627il+aEOXRN5xr4Hl+kRNITD1tPvYejVryXKJw/aPWN/A0bV0wiUQCGM52puwKu5v8hQJejs+kSc1l8FQ5601WOcleQpRCOJp0XPYF5Eaud7bmGnkKISqD00rR1rpi58bdDfwl8CyQKX45QpRf31AUt0tx7rqAJc/XHaznx3tHSKYzjrvys5YMmCNU26wboWra0uZHKXg+HOcN26wvURHW+acbty95n9lA0643OSHjdUdjKc5qrrNlDQ4hewlhuZFo0rFvOrqC9fPTz4QQznemvYQov0LHqJqiWusfa61HtdbHzA9LViZEmewZjnJ2u9+yYEN3m4+shsPH5OqKnfojU/i9VbQHrJtAYqqtcbO5pV4aeVa4ESMDw65Geu3G667lRp4G2UsIy9k1IjkfXUEfQ8dnSM1JvE4IIVaq2ADGo0qpO5VSlyulXmp+WLIyIcpAa83e4SjbOqzJvoAFo1SlD4atDkTiJZlAYupt97M/IgEMp/vYA8/xsQeeW/S+cCxJjdtFc11NmVeVY2ZgmKUsa5jsJYSlkukME9OzrHNgCQnkMjWzGo4cm7F7KUKIPJxpLyHKr9gSkkuNzxcvOKaB1xT5vEKUxfBkgonpWbZtaLTsObtafSiVG6Uq7DMwOsVrz2kv2fP3hvzs3BcmMZspevyuKJ19R2NL3heJJmkLeHDZNEnGDGBEJAND9hLCUqOxFHAiy8lpjhgZmq//zGN0NNZy69W9XLe9w+ZVCSGWcqa9hCi/ogIYWutXW7UQIezQZ9SgXmBRA0/IlRd0NNbKKFUbHZtKcWx6tiQNPE1bQ360zgVKtlkwwUaU30g0OR9EsEOgtgpPlWvNZ2DIXkJYbSSaG0/sxB4Y9+0e5gs/G5y/PTyZ4LZ7+wAkiCGEEHkodgrJ3y52XGv98WKeV4hy6RuOUuVS9IasbfTYHfTJKFUb9ZsNPNutb+BpMv+feT4ckwBGhYrEkpZNHyqEUopQg3fN98CQvYSwmvkz5cQAxp0795NMZ086lkhnuHPnfglgCCFEHortgTG94CMDvAHYXORzClE2fcNRekPWNfA09bT5ODg+RTarLX1ekZ/5AEZb6TIwNrXU4612SSPPCqW1tj0DA3ITUKSERPYSwlrh+Qa9tTav5HRHJxMrOi6EEOJkxZaQ/OPC20qpTwP3F7UiIcpEa82eoShv3Bay/Ll72nwk01mGJxNrfTyiLQYicXyeqpJefXO7FFvapJGn03UF6xc9Hk2kSc1lbZ9SEAp4+c2Lk7auwW6ylxBWG4km8Xmq8HmKbfVmvfWNtQwvEqxY3+i8YIsQImepvYSwh9W/2euALoufU4iSGDqeIJpIlySFvDtoTCIZm5IAhg36R6foKeEEElNvyM/PD4yV9DVEce64/oJFj5sp5rYHMBq8hJ9LorUu+f+vFUT2EqIo4ahzR6jeenUvt93bRyJ9YoRqbbWbW6/utXFVQogzWWovIexRVAmJUqpPKbXH+HgO2A981pqlCVFae+YbeFo3gcRkjlKVPhj2OBCZKmn5iGlryM9YPMXE9GzJX0tYa8RMMXdACcnsXJbJmbSt67CT7CWE1UZiSUf2v4Bco847rt/GemN99R43d1y/TfpfCCFEnorNwHjzgq/ngIjWeq7I5xSiLPYMT1LjdnF2yPo3us31NTTVVTMok0jK7vj0LONTqZJOIDEtbOR5RXdryV9PrNxt9+4BTr96Eok6JAPDCKCEY0ma6mtsXYuNZC8hLBWJJjm7zbm/k6/b3sF12zt4x11PEk+lJXghhMMttZcQ9igqA0NrfWTBx3A+Gw6llFcptUsp9Vul1HNKqY8ZxzuVUk8ppfqVUt9XStUYxz3G7QHj/s0Lnus24/h+pdTVC45fYxwbUEp9tJjvUaxee40Gnp4qaxt4mnrafAxIBkbZmeNrt7SVbgKJyQxgSCNP5zo4Ns3BsenTjpslJG1+u0tIPABrehJJIXsJIZYyl8kyGnduCclCl3Q2s+9ojHhy7WZgCVEJltpLCHsUO4WkECngNVrrlwAXAtcopS4DPgV8Rmu9BTgOvNs4/93Aca11D/AZ4zyUUucCNwLnAdcAX1BKuZVSbuBfyHUxPxd4h3GuEPPMBp6lHH/ZHfQxKL/syq4/Yo5QLX0GRtDnoamuWgIYFSgcTdLqq6Gmyo4/gyeYARQzI0RYQynVqJT6oVLqeaXU75RSlyulmpVSDxsXSh5WSjUZ5yql1OeMix57lFIvXfA8Nxvn9yulbrbvOxL5GptKkdX2Z1fl49LOZrIanj1y3O6lCCFExSj7zk3nmJelq40PDbwG+KFx/BvAdcbX1xq3Me6/SuU6nV0LfE9rndJaHwIGgB3Gx4DW+qDWehb4nnGuEPOOHJshnpzjghI08DT1tPmYmJ6V/ghldiASp67GzfoyjM9TStEb8vO8BDAqTjjmjCu07QtKSISlPgv8RGu9FXgJ8Dvgo8AjxoWSR4zbkLvgscX4uAX4IoBSqhm4HbiU3N7idjPoIZzL7G/j1B4YC23f2EiVS7Hr0ITdSxFCiIphy6UnI1PiN8Ao8DAwCEwuSBsdAsyCwA7gRQDj/ijQsvD4KY9Z6vhi67hFKfWMUuqZsTGZJLCW9A3nGniWYgKJqdtoIillJOU1YEwgcbnKM9FhayjAgUicbFaX5fWENcLRpO0NPAFqqly01NcQkQCGZZRSAeAVwN0AWutZrfUkJ18QOfVCyTeNCyxPAo1KqXXA1cDDWusJrfVxcvuVa8r4rYgCzPe3CTh/LGldTRXbNjRIAEMIIVbAlgCG1jqjtb4Q2EDuqsY5i51mfF7sXYgu4Phi67hLa32x1vriYDC4/MLFqtE3HKWmysXZ7aXrk9BjjFKVRp7l1T8an58CUw69IT8zsxmGjifK9poif+euD3Du+sBpx8Ox5Hz2g93aA14isZTdy1hNuoAx4GtKqd1Kqa8qpeqBdq31CIDxuc04v+gLIsI5RhzSoDdfOzqb+e3QJMkFY1WFEM6y1F5C2MPW4l/jisjPgMvIXfEwp6JsAI4aXw8BZwEY9zcAEwuPn/KYpY4LMW/P0CTnrAuUtP69o7EWT5VLMjDKKJpIE4mlShqYOtXCSSTCeW5/y3nc/pbzTjqWTGeYnEk7JsU81OAlLD0wrFQFvBT4otZ6OzDNiXKRxRR1QUSyOZ0lHEtSU+Wiqa7a7qXkZcfmZtIZze4XJu1eihBiCYvtJYR9yh7AUEoFlVKNxte1wGvJ1aY+CrzNOO1m4EfG1/cbtzHu/6nWWhvHbzSmlHSSq13dBTwNbDGmmtSQa/R5f+m/M1EpslnNc8MxtnWUNpLqcim6gj7JwCijgdFcL4otZczAMIMl0sizcpjBAmdlYEgAw0JDwJDW+inj9g/JBTQiRmkIxufRBecXfEFEsjmdZSSaZF2Dl1y7NOe7eFMzSsHTh6WMRAgh8mFHBsY64FGl1B5ywYaHtdYPAn8NfFgpNUCux8Xdxvl3Ay3G8Q9jXEXRWj8H/ADYB/wE+IBRmjIH/Bmwk1xg5AfGuUIAcPjYNPHUHBd0NJb8tWSUannNTyApwwhVk89TxVnNtTwfkQCGE33oe7v50Pd2n3TMbJi5rgyNXvMRCng5Nj1Lak5SyK2gtQ4DLyqleo1DV5HbKyy8IHLqhZJ3GtNILgOiRonJTuD1Sqkmo3nn641jwsEiDulvk6+Gumq2hgLSB0MIB1tsLyHsU7X8KdbSWu8Bti9y/CC5fhinHk8CNyzxXJ8APrHI8YeAh4perFiVzAaepRyhauoJ+nhwz1ESsxlqa9wlf721rn90Cm+1iw1N5X1j2tsekAwMhxpZpDTDzHYINXjKvZxFmesYjaU4q7nO5tWsGn8OfMfIxDwIvIvcRZsfKKXeDbzAib3FQ8AbyU0zmzHORWs9oZT6O3IXWwA+rrWWd5kONxJL8NKNlTUs5tLOZr7/9IukM1mq3faOdhZCnG6xvYSwT9kDGELYrW8oiqfKVZYyg+62erSGg+NTnLe+9AGTte5AJF7WCSSmrSE/j+4fJTWXwVMlgSqnG3FYCUmbsY5ILCkBDItorX8DXLzIXVctcq4GPrDE89wD3GPt6kSpZLOaSDRVMQ08TTs6m/n644fZOxxle4UFX4QQotwkzCvWnD3DUc5dH6CqDFc5emSUalkNjE6VtXzE1Bvyk8lqBkeny/7aYuXC0SQ+TxV+rzOa/Jnp7mHpgyFEUSZmZpnNZFnnkOBkvi7Z3AwgZSRCCJEHCWCINSXXwDPKBR3lyYbY3FKPS8HgmLyxLbV4Ms1INFnWEaqmrcYkkv0RmURSCcLRJO0BZ5SPwIIAhqSoClGU8PwIVWf0t8lX0O+hq7VeGnkKISx33+5hXvbJn9L50f/gZZ/8KfftHrZ7SUWTEhKxphwcn2Z6NsP5ZQpgeKvdnNVcx6BkYJScmeVSzhGqps2t9dS4XTwvfTAc56WbTk/HDseSjkoxb6yrpqbKxWg8ZfdShKhoJwIYzvn5zteOzmYe6hshm9VlL4MUQpzZYnuJSnDf7mFuu7ePRDrXJHx4MsFt9/YBcN32DjuXVhQJYIg1pW84N2f9gg2ln0Bi6pZRqmVxYgJJ+TMwqt0uutt80sjTgf76mq2nHYvEklzR3WrDahanlCIU8EoGhhBFGpmfMFSZAYzvPf0i+yNxzllX2jHvQoiVWWwv4XRaa/7Pf+ybD16YEukMd+7cX9EBDCkhEWvKnqEotdVuuoP1ZXvNnjYfB8enyWR12V5zLeofjVNT5bKtCeLWkF8CGBUgk9WMxlOOmUBiCgW80gNDiCKFowncLkWrz1k/3/nY0Sl9MIQQxctmNT/ZG+a6f/kV41Ozi55zdDJR5lVZSwIYYk3ZW8YGnqaeoI/ZuSwvTsyU7TXXov7RKbqDPtw2pd72hvyMRJNEZ9K2vL5Y3Pu+9Szv+9az87fHp1JkstpxNfLtDd758a5CiMKEoyna/R7b/g4UY0NTHR2NtRLAEMKBTt1LONHsXJZ/feZFXveZn/O+bz/LZCJNY+3izcrXNzprD7RSEsAQa0Ymq9k7HGNbmfpfmLrbctkeUkZSWv2RKc5uL3/5iKl3vpGnZGE4yfGZWY7PnLgCMV8j77ApBaGAh3A0SW6ipxCiEOFYgvYKLB8xXbK5iV2HJ+T3gBAOc+pewklmZue455eHeNWdj3LrD/dQU+Xmc+/YziMffiX/+63nUVvtPun82mo3t17da9NqrSEBDLFmDI5NkUhnuGBDmQMYQRmlWmrTqTmGJxO29L8wzU8iCcskEicbcWgAoz3gJTWXJZqQDB4hCjUSTVZk/wvTjs4WxuIpDh+TjE0hxJlFZ9J87pF+rvzUo3z8wX1saKrja++6hIc+eCVvfcl6qtwurtvewR3Xb5vf8wS8Vdxx/baK7n8B0sRTrCF9Q1GAsmdgNNbV0OqrkQyMEjKDQz1t5Z9AYgoFvPi9VTKJxOHMMg2nTSloN0epxpI01tXYvBohKo/WmnA0yavObrN7KQU70QfjGJ2t5evVJYSoHJFYkrt/eYjvPHmE6dkMV21t409f1c3Fm5sXPf+67R1ct72Dqz/zGG0BT8UHL0ACGGIN6RuOUlfjpitY/qv03UGfZGCUUL/x33aLjSUkSilp5FkBRqJJqt2KlnpnBQnMgEo4mmRrSCYQCLFS8dQcM7OZis7A6A7W01Jfw1OHJnj7JRvtXo4QwkEOj0/z5ccG+bdnh5nLZnnLS9bzvld25z216PLuFr739AvMzmWpqarsIgwJYIg1Y8/QJOevb7CluVdPm48HfnsUrTVKVV5zMafrH41T43axyaYJJKbekJ8f/Ub+nZ3kZT0nj0uNxJK0+b24HNbkz0zvHI2lbF6JEJXJ7G9TyT0wlFLs6GyWRp5COMype4lyeu5olC/+bJCH+kaocru44eINvPcV3WxsWdme94ruFr7++GF+8+LkfLZXpZIAhlgT5jJZ9o3E+G87Ntny+t1BH7HkHONTswT9lTfezen6I1N0BevLOl1mMb2hAPHkCxyNJumo8A7Pq8UHr9py0u1wNOm48hGAtkDu94KMUhWiMGZ/m0rOwAC4ZHMzP94b5uhkouInBQixWpy6lyg1rTW7Dk3whZ8N8vMDY/g8Vdzyim7++MrNtPkL+x13aVcLLgWPD45LAEOISjAwNkUynS17A09TT9uJRp4SwLBe/2icl2xotHsZJzXylACGM4VjSc7NM92ynDxVbprraySAIUSBwtEE4LwGvStlvrF4+vAE115Y+bXqQogzu2/3MHfu3G8ELb28Yds6dr8wybNHjtNSX8OtV/fyR5dtomGJkaj5aqit5vyOBh4fPMaHXmvR4m1S2QUwQuRpj9nA06YARrcRwJBGntabmZ1j6HiCs9vta+BpMtcgjTyd4+Z7dnHzPbuAE03+2h36Bqc94CUSlQCGEIUIR3PlV079+c7XOesC+D1VPCVlJEI4xsK9hJXu2z3Mbff2MTyZQAPDk0m++otDDI5O8fFrz+OXf/0aPvDqnqKDF6bLu1vY/cJxErMZS57PLhLAEGtC31AUn6eKzhZ7unqvC3ipq3FLI88SGBydRmtsHaFqaqitZn2DVxp5OkgynSGZzv2hjiXnSKSd2+QvFPBIBoYQBQrHErT6aiq+OZ3bpbh4c5P0wRDCQRbuJax05879JBZ53jqPm3devpnaGrelr3dFdyvpjOaZI5X9+6Wyf8sLkae+4SjnrQ/Y1rjP5VJ0BeslA6ME+kdzwQI7J5As1CuTSBzL6U3+2gPe+TGvQoiVGXFof5tC7OhsYWB0ivEpaeorxGp2dDKx6PGRydLsBS7Z3ESVS/H44LGSPH+5SABDrHppo4GnXf0vTD1BH4OSgWG5/tEpqt2KTTZl15yqNxRgcGyKdCZr91LEKczsBqdmYLQHvIxPzTI7J//vCLFS4WiSUGB19B4y+2A8c7iyr5IKIZY2dHxmyQurpWrgW1dTxfaNjRLAEMLpDkTizM5lOb/D5gBGm4+j0STTqTlb17Ha9Eem6Gytp9rmCSSmrSE/6Yzm0Pi03UsRp3B6kz/z6vGYXHUVYsXCsaRjg5Mrta2jAW+1i12Hjtu9FCFECQyOTfEHX3qCahd4Til7q612c+vVvSV77cu7W+kbmiSWTJfsNUrNGTt+IUpo73CugecFNk+p6A7mShwOjskbWyv1j8bZ0mZ/A09Tb0gaeTrJVee0cdU5bcCJJn/myFKnMQMrYWnkKcSKJGYzTM6kV00JSU2Vi+1nNbHrcGVfJRVitVi4lyjWvqMx/uBLTzCbyXLv+6/kU79/AR2NtSigo7GWO67fxnXbSzeB6IruFrIadh2s3AwvGaMqVr09Q1H83io2NdfZuo75Uapjcdumoaw2yXSGFyZmuM5Bo+a6gz6qXIr94Ri8ZL3dy1nzbnlF9/zX4ViSlvoaPFXWNsWyijk9QfpgCLEyZnmYU7OrCrGjs5nP/7SfWDJNwGvNBAIhRGEW7iWK8eyR47zra7uo91Tx7fdcSnfQx7nrAyUNWJxq+8ZGPFUuHh88xmvPbS/b61pJMjDEqtc3HOX89Q22NfA0bWqpx+1SDI5KBoZVBsemchNIHNLAE3JXzrqC9dLI04HC0YSjRyyaV48lA0OIlTF/ZlZLCQnApZ3NZHXuDY8QovL9amCcm+5+iub6Gv71fZfPZ2aXm6fKzSWbm3l8cNyW17eCBDDEqjY7l+X5kbjtDTwh98Z2U3OdjFK1kPnf8ux255SQQK6Rp5SQOMPbv/wEb//yEwCEYylHp5g31VVT43ZJBoYQKxSOGf1tHPzzvVLbN+amBcg4VSHst3AvUYiH90V419ee5qymOn7wvsvZ0GRvVvjl3S08H45zrEJ7bkkAQ6xqByJxZjNZx5RsdLf5GJBRqpY5EInjdik2O2QCiWlryM/Q8QRT0rDVUSIxZ49ZVErRFvDMp8MLIfIzYmRgOPnne6Vqa9xs29AgAQwhKtyPfjPM+779LOesD/D9915Gm9/+31NXdLcA8GSF9sGQAIZY1fYM5Rp4brN5AompO+jjyLFpGbFpkf7IFJtb6qipctavsl4jI0TKSJwjmc4wMT3r+Br5UMArJSRCrFA4miTgraKuZnW1dtvR2cyeoUmS6YzdSxFCFOA7Tx3hQ9//DZdsbuI777mUxroau5cE5N4X+TxVFVtG4qxdvxAW6xuOEvBWsdHmBp6mnjYf6YzmhYkZu5eyKgyMTjmufAROTCKRAIZzjMZyaZJOv0Lb3uCVEhIhVigcTbKuodbuZVju0s5m0hnN7hcm7V6KEGKFvvzzQf7nv+/l1b1tfP1dO/B5nBNgrXK7uLSzmScGK3PSkQQwxKrWNzzJBRsaUcreBp6m7mCu1GFQ+mAULTWX4fCxaba0OaeBp6mjsZb6GnduEkmJ3bd7mJd98qd0fvQ/eNknf8p9u4dL/pqVaCRq1MhXQAZGJJZCa233UoSoGGGHl4cV6qJNzSiFlJFYSP5milLTWvPpnfu548fP8+YL1vHlmy7CW+286WeXd7dwcHx6fn9USSSAIVat1FyG/eE45zukfARyPTAA6YNhgYNj02Q19DgwA8PlUpwd8pe8ked9u4e57d4+hicTaGB4MsFt9/bJhmyBN1+wjjdfsO7EmEWHv8kJBbwk0hliSemfIkS+RqJJxwcnC9FQW805oQC7DlfmVVKnkb+ZolDmXmI52azmYw/s458fHeDGS87iszdup9rtzLfbV3S3AlRkFoYz/4sKYYH94TjpjHbEBBJTwFtNm98jo1Qt0G9ksTgxAwNyjTz3R+IlvZJ+5879JE6pjU6kM9y5c3/JXrPS3HT5Zm66fPN8WYbTAxjtxvqkjESI/KQzWcannD1hqBg7Opv59ZFJ6Z1lAfmbKQpl7iXOZC6T5SP/toevP36Y91zZyR3Xb8PtckYG+GK2hvw01VXzuAQwhHAOpzXwNPXIJBJLDETiuBR0BZ01gcTU2+5ncibNaLx0I6qOTi6e9rfU8bUoMZshMZthJJqkrsaN30E1qIsxryJLI08h8jMaT6E1rFvFAYxEOsPe4ajdS6l48jdTFMpPq7GYAAAgAElEQVTcSywlNZfhz7+7mx8+O8RfvvZs/uebznFM+fpSXC7F5d0tPDF4rOLKViWAIVatvqEojXXVbGhyVmOvnjYfg6NTFffLwmn6R6fY3FKPp8p5dYUAvaEAQEnKSLTW/ODpF1nq/6D1jc76f95O//1ru/jvX9uVG6Ea8Dp+QzEfwJAMDCHyEjb726zSAMYlm5sB6YNhhfYlyozkb6ZYjrmXWExiNsOffPNZfrw3zN+86Rz+4rVbHL/XMF3e3crwZKLihguUPYChlDpLKfWoUup3SqnnlFJ/YRxvVko9rJTqNz43GceVUupzSqkBpdQepdRLFzzXzcb5/Uqpmxccv0gp1Wc85nOqUv4vEpbqG46yraPBcb9EuoM+plJzJb0yvxYciMTpcWj5CORS8wDLG3lOpeb4y+//ho/82x62tNXjPWWErLfaxa1X91r6mqtBOFoZTf7aAh4AIpKBUTSllFsptVsp9aBxu1Mp9ZSxZ/i+UqrGOO4xbg8Y929e8By3Gcf3K6Wutuc7EWcyEq2M8rBCBf0euoL1EsCwwOaW0yfS1bjlb6YoXCyZ5uZ7dvGL/jE+9fvbeM/Lu+xe0opc0d0CUHFlJHZkYMwBf6W1Pge4DPiAUupc4KPAI1rrLcAjxm2ANwBbjI9bgC9CLuAB3A5cCuwAbjeDHsY5tyx43DVl+L6EgyTTGQ5E4o7qf2Ey33QPyCSSgs3OZTl8bIYt7c4NYDTV19Dm97A/bN2/83NHo7zl87/k/t8e5a9edzY/+dAr+eTvX0BHYy1mmO6N29Zx3fYOy15ztQhXSJM/b7WbxrpqycCwxl8Av1tw+1PAZ4x9xnHg3cbxdwPHtdY9wGeM8zD2JjcC55HbR3xBKeXMlK81zCy3WhdYvVfRL+1sZtfhCTJZydws1N7hKE8dnuDVva3zfzOr3QqF5tz1AbuXJyrQxPQsf/iVp/j1C8f53I3befslG+1e0op1tdbTHvBIAGM5WusRrfWvja/j5DYXHcC1wDeM074BXGd8fS3wTZ3zJNColFoHXA08rLWe0FofBx4GrjHuC2itn9C5HP1vLngusUb8biTGXFY7rv8F5DIwAAbL3AdjNY0OO3xsmkxWc7YDJ5As1Bvysz9SfAaG1ppvPXGY3/vC48zMzvHdP7mMP79qC26X4rrtHfzqo6/h0CffxEWbmth1aII5afZ2mtF45TT5y41SlQBGMZRSG4A3AV81bivgNcAPjVNO3WeY+48fAlcZ518LfE9rndJaHwIGyF0wEQ4SjiaprXYTqHV2f5ti7OhsJp6cY3+JJ1utVlprPvbAczTX1fDZd7x0/m/mYx95NYG6Gt79jaeZmJ61e5migkRiSd7+5Sc4EIlz1zsv4i0vWW/3kgqilOKK7laeGByvqNJ2W3tgGGma24GngHat9QjkghxAm3FaB/DigocNGcfOdHxokeNiDTGbXW3b0GjzSk7XHvDg81SVNQNjtY0OOxDJbeKcXEICuTKS/shUUVfNook07//Or/lfP3qOK7pbeOiDL+fSrpZFz33vK7oYOp7gx3vDBb/eapTOZJnL6ooJYLQHvERiUmJWpH8CPgKY0bwWYFJrbc6nXbg3mN9PGPdHjfOX2mcIBxmJJVnX4Pz+NsU40Qejsq6SOsWDe0Z4+vBxbr26l4C3ev74uoZa7rrpIiKxFH/67WeZnZPgv1jeC8dmeNuXHufoZIKvv2sHr9nabveSinJ5dwvjU7Pz0/0qgW0BDKWUD/g34ENa6zNdolzsL5Iu4Phia7hFKfWMUuqZsbGx5ZYsKsieoSgt9TWsd+AbFqUU3W2+sgYwVtvosP7IFC51IpvFqXpDAVJzWQ4fK2xs7u4XjvOmz/2Ch/dF+P/euJV7br6EFp9nyfNfe047XcF6vvzYYEVF0kvpbRdt4OVbcrPOl2rg5jShgFdKSIqglHozMKq1fnbh4UVO1cvcl9d+QvYS9gpHkxXzs12oDU11dDTW8vTh43YvpeIkZjPc8dDvOG99gBsuPuu0+7dvbOIffv8Cnjo0we3375W/neI09+0eZn84zlOHJtjxif/izZ9/jFhiju/8yWVc3r34BaVKMt8HY2Dc5pXkz5YAhlKqmlzw4jta63uNwxGj/APj86hxfAhY+BtnA3B0meMbFjl+Gq31XVrri7XWFweDweK+KeEofcNRzndgA09Td7C+rCUkq2102MDoFBub6/BWO7sc/UQjz5Wl/Wazmq88dpAbvvQEWsMP3nc5t7yiG9cy88RdLsUtL+9i73Cs4uoZS+WGi8/ivPW5UrJKGbPY3uBlfCpFWkqBCvUy4K1KqcPA98iVjvwTuRJUs85g4d5gfj9h3N8ATLD0PuMkspewVziarJif7WLs6GzmqUMT8gZ7hb782CBHo0luf8t5uJf4G3rd9g7e/6puvrvrRb7++OHyLlA4mpnBPJlIA7ly1Fgyw3tf2cWFZzkvy7sQG5rq2NhcV1H7RjumkCjgbuB3Wuv/f8Fd9wPmJJGbgR8tOP5OYxrJZUDUKDHZCbxeKdVkNO98PbDTuC+ulLrMeK13LngusQYkZjP0j045soGnqafNRySWIpZMl+X1zMkGp6rU0WH9o3F62pzd/wJy/84utbJRqhPTs7znm8/wiYd+x1XntPHQB1/OSzc2Lf9Aw3XbOwj6PXzp54OFLHnVmZienQ8WVkITT8itU2sYk0lFBdFa36a13qC13kyuCedPtdZ/CDwKvM047dR9hrn/eJtxvjaO32hMKekk1xR88Tl6whbZrM6NSF4jAYzxqRSHxgvL6FuLhicTfOnng7z5gnXs6Gw+47n/4/W9vO7cdv7uwX08dkAyqUTOYhnMAN958gUbVlM6V3S38OTBYxXTKNiODIyXATcBr1FK/cb4eCPwSeB1Sql+4HXGbYCHgIPkmmd9BXg/gNZ6Avg74Gnj4+PGMYA/Jde4awAYBH5cjm9MOMO+kRgZhzbwNJmlDwfHSr8R0VrTWFt92nFPVWWODktnshwan3b0BBKTt9rN5tb6vEep7jo0wRs/+wt+2T/Ox956Hl/6o4toqDv9326513zXyzbzi/5xnjsaLWTZq8qffvtZvvXEEdwudcbyGycJNeTWKWUklvtr4MNKqQFyPS7uNo7fDbQYxz+MMQVNa/0c8ANgH/AT4ANa69N3ssI249Mp5rJ6zWRgADJOdQU++ePn0Rpue+M5y57rcik+8/YLObvdzwf+76/L3mhdONNqy2BeyuXdLcSSc+w7Wnzj+XKwYwrJL7XWSmt9gdb6QuPjIa31Ma31VVrrLcbnCeN8rbX+gNa6W2u9TWv9zILnukdr3WN8fG3B8We01ucbj/kzLfl2a8Z9u4f546/nLpD97Y/2OrZJZTlHqT64Z4T9kSmu375+fnSYAnrbfRU5bvPIsWnSGc0WhzfwNG0N+ZctIclkNf/8035uvOsJvNUu7n3/Fdx8xeaCS6D+8NJN1Ne4ueuxgwU9frWZzWRp93uWTB92mjZ/7s1YJCoBjGJprX+mtX6z8fVBrfUOY89wg9Y6ZRxPGrd7jPsPLnj8J4y9RK/WWi6GOIw5QnW198CA3LjDVl+NBDDytOvQBA/89ijve2U3HXlmm/o8VXz15oupcbt4zzeeYXJGJpOsdUtlKldqBvNSzF4ejw9WRh8MW6eQCGEls04tmsg1mQ/HUo6dtLGxuY4qlyp5hD+WTPPxB/exraOBO2+4cH502F+9/mz2DMcqqmGPqT+S+2/m9BGqpt72AEcmZpiZnVv0/tF4kpvv2cWn//MAb7pgPQ/8+ZWcX2T2UENtNf/t0o08uGeEFydminqu1WB2Lkt7BV2hNdPhJQNDiDMbMQIY6xpW15uJxSiluGRzM7sOSwBjOZlsbmzqugYv73tl94oeu6Gpji/ddBFDx2f4wP/9tfQiWuNuvbqXavfJFz9qq90VmcF8Jm1+L1vafBXTB0MCGGLVqKRJG9VuF5tb60uegfHpnfs5NpXi739v20lXn9/z8i42NNXysQf2MVdhf5z7R6dQFTCBxNQb8qH1icDLQr/sH+eNn/0lTx+e4JPXb+NzN16I37uykpGl/PGVnSjg7l8esuT5KtlsJlsx/S8AmutqqHYrCWAIsYyI8TOyFnpgQK6MZOh4guFVlr5utR8++yLPHY1x2xvPobZm5c2+L9nczN//3jZ+NXCM//PgvhKsUFSK67Z30NVaP3+7o7GWO67fVpEZzMu5oruFpw9PVMQ4YQlgiFWj0urUeoI+BksYwPjti5N868kjvPPyzWw7paGpt9rN37zpHPZH4nx3V2U1IjoQibOhqbagTYkdekMB4ORJJHOZLP/4n/u56Z6naKyr5v4/u5Ibd2y0dGrOuoZarr2wg+8//SLHp9d2GuzsXLai3uC4XIo2v5fRmDTxFOJMRqJJqt2Klvoau5dSFmYfjKeljOT/tXffYVGd2QPHvy8dREUUG6ICKvaCvUdNLDFGoybZbBI32U0xm7JpJjHll41uNsX03vumGmNMsxt7iYpdQRALTUAFRanD+/tjBoMKCjIz917mfJ6HB7jM3DnAnZn3nvu+51TqeEExsxbE06tVA8Z1bXbR+7m6VwS3Do7k07UH+GLdASdGKKwkPTefhMw8wkMC6RsZyupHhtfK5AVA/+hGnCqysS0lx+hQLkgSGKLWaB5S8QmKWdepRTeuw4Gjp1yS6SyxlfLoD9sJC/bngZHtKrzNqE5NGRDdkBcXJVhqnWdiZh5tLdCBpIy93avX6U4k6bn5/PX99by+NJHJsS2Yd9dAYpq65ve5bUgU+cU2jx58TerZglJtnQ4kZZrWDzi9vl8IUbGM3AIa1w24YIvp2qJ903rUDfBhvSQwKvXG0kSOnCziyXGdanxR4JExHRgWE8aT83ZacsmtqLk5m1PRGm4ZHMkN/VoZHY5L9YsKRSkssYxEEhii1hjdqek528y8Tq1N42BspZoDR5zfieSztQfYmXacJ8d1qnRJglKK/xvXkeP5xby8KMHpMbhCia2UfVnW6EBS5qetadhKNR+tTqbnzEWMeOF3dqTl8vK13Zh1dTeC/Hxc9tgxTesyLCaMT9bsp6CCNmCeoIejT7uVZmCAPeFyWJaQCHFe6bn5HtGBpIy3l6MORrL5TzCMsC8rj49XJ3NNz4hzZp5eDG8vxWvX9SCqUR3u+N9m9tfiFrZz41IZ+OxSIh/5hYHPLjVl/Th301rz/aYU+kSGcvPASMZ1a250SC4VEuRHp+b1LFHIUxIYolYosZWyfG82YXX9aB4SgML869TKajg4u5Bnem4+Ly6M55KYMC7vcm5Sp7z2Tetxfd9WfLH+4AU7ZZjBwaOnKLKVWmYGRllh2WKbvRHSkZNF5BeX8q9L23JVjxZuieH2odEcOVnE7E0pbnk8s9mRam8la7UZGE3qBZBxvABpoiVExebGpfLH/mNsPHDMo064ercOJSnrJNl5ssTsbE//sht/H28edOKFq7oBvnzwt14oBbd8tpHjBcVO27dZlI1VUnPy0UBqTr5pi+C70+aDOezLPsnkni1Iy8k37ZJ0ZxoQ3YjNB3JMf9FLEhiiVpizOZXEzDxmXNmZNY+MIPnZsaZfp1aWwHB2Ic8ZP+2ipFQz48rOVZo+ef9l7Qj29+Gpn3aa/mQpwVEI0yotVCsqLKuBz9a4b0lH38hQukWE8P7KfdhKzf3/dYXXlyYCFpyBUd+fU0U2ThRW3L1GCE9mP+Hadvo1zZNOuMrqYGyUbiRn+D0+kyV7MrlnRBvC6vo7dd+tGtbh7et7sj/7JHd/GVfr3kutVATfnb7fnEKgrzeXd2nGfd9s4b5vthgdksv1j25Ika2UTQeOGR3KeUkCQ1heQbGNlxcn0C0ihNGdzz/jwEzq+PvQvH6AUxMYS/cc5rcdGdwzoi0tGwZV6T4N6vhx/2XtWJN0hAU7DzstFldIzLTPEmljkQSGGQrLKqWYOiSKA0dOsWBnhtse1yyKHF12mlhwBgbAYamDIcQ57CdcZ9aP8pQTri7h9Qnw9ZI6GOUU20qZ+fMuIhvV4aYBkS55jP7RDZkxvjPLE7J45tfdLnkMo5hhrGI2BcU2ftqaxpjOTQn2d91SX7Pp3ToUHy9l+mUkksAQlvfZ2v2k5xbw8OgYp3ZxcIfoxsEkZTlnTWV+kY0n5u6kbeNgbh0cVa37Xt+3Je2aBPP0r7tMPW1sr6MSdB2LvJlUVkDW3YVlR3ZqSuuGQby7PMn0s2ycaW5c6ukB2IgXl1vq6mxZAkNaqQpxLk8+4fLz8SK2ZQM2SALjtM/XHiAp6ySPj+2An4/rTm3+2rclNw1ozQerkvnmD2t1cDsfs4xVzGThrsOcKChhck/3LPc1i2B/H7pFhJi+kKckMISl5eYX8+ayJIa0C2NAdCOjw6m26LBgkrLyKHXCdMRXl+wlNSef/0zoXO03cB9vL54c14lDR/P5cFVyjWNxlYTDeZYq4DltVAyBvme2ezWisKy3l+LWIVFsTcll3T7PGPSWrekte2pZbYp5Wc0O6UQixLkqWxLmKSdcfSJD2ZV+vFbWY6iuI3mFvLw4gSHtwhjevrHLH+/xsR0Y3LYRj8/dUWuSSPdd2paKLv9N6V+7u26cz+xNKYSHBNIvqqHRobjdgOiGbEvJ5YSJX18kgSEs7Z3lSeTmF/PwaHN2GrmQ6MbBnCqy1fgqa3zGCT5YuY+re7ag70W+2A5s04hRnZrw5rJEU5402Uo1SVl5lql/ATChRzjPTOxCeEig4YVlJ8W2oFGwH++tSHL7YxvB6mt6y07QMk9IoT4hztaxWb1ztpm565iz9Wkditawab+516m7w0uLEjhVZOP/rujgllm4Pt5evHFdLBENgpj6xSYOHT3l8sd0tbzCEjTQsI4fCnsCPdDXiyW7M51ygc1qMnILWLU3i4mx4R7Torm8/tENsZVq/jBxnR1JYAjLOny8gI9XJzO+e3M6Na95uywjtHFCIc/SUs1jP2ynboAP0y/vUKN4Hru8IyWlmufm76nRflzh0NFTFJWU0raJNTqQlJnQI5zVjww3vLBsgK83Nw1ozbL4LPZkHDckBney+hTzAF9v6gf6mjKZKISRMk8UsDopm9iWIaZIDhuhR8sG+HgpNpj4BMMddqUd56sNB5nSvxVt3NidrH6QvTNJia2UWz7dSJ6Fiy2fLCzhjWWJ9IsKZePjl5L87FjWPTqCp67szIb9R/n6j0NGh+h2P8SlUqrtF37K3Do4qtrLs60qtmUD/Hy8WJNo3mUkksAQlvXK4r3YSjUPXGbdKy7RjesANWul+t2mQ2w8cIzpl3cgtI5fjeJp2TCIWwdH8kNcqqkqEM+NS2Xi22sAmDU/3jLLAMzmhn6tCPLz5r0V+4wOxaXScvLxruSqiVWmmM+NS+VkYQmfrzvgUS0ihbiQt39PotimeeHqbqZIDhsh0M+bri3q15olDBdDa82Mn3dSP9CXe0e0c/vjR4UF8+b1sSRm5XHv19btTPLRqmSy84p4aHT7M2awXN2rBf2iQnnmt91kelAtJq01szcdonfrBrRuVOf09ks7NuHSjk0MjMx9Any96dWqganrYEgCQ1hSUlYe3248xF/7tKxytw0zCgv2p16Az0XPwDiSV8gzv+2hT2QoVzup0NA/L2lDk3r+zPhppymmDpbVMjh6sgiArLxCS9UyMJOQID+u7R3BvC1plpmJUF2JmXlMfnsN3opzasFYZYp52TFf4oEtIoU4n7ScfP637iCTYsOJCrPOckJX6BPZkG0pOeQXmbfwtivN35HBun1HeWBkDPWDfA2JYXDbMP7vio4s3p3JCwutsTyxvGMni3hvxT4u69iE2JYNzviZUopnJnalsKSUf/+006AI3W/LoRySsk6eU7wzKSuvRhcbrWZAdEN2pR/nmGPsbTaSwBCW9OLCePx9vLhreFujQ6kRpRRtGgdfdALjv7/u4WRhCf+9qrPT1n7W8ffhkTHt2ZqSy/ebU5yyz5qwei0Ds/nHoEg09qsutc3WQzlc/c4aimylfP/PgTw/qSt+3va3OStNMZdjXoiKvbEsEY3mbou/9ztD38hQim2auEPmmS3pLgXFNp7+dTftm9bluj4tDY1lSv9W/LVvS97+PYk5JhgzVcfby5PIKyqpNLEf2agO/xrRll+3Z7Bo12E3R2eM7zenEODrxeVdmp2x/dE523l0znaDonK//o7GCOv2mXMWhiQwhOVsOZTDr9szuGVwFGF1/Y0Op8bsnUiq30p1bdIRvt+cwm1Dopy+9nN8t3B6tAzhufnxhlchtnotA7Np0SCIcV2b8dWGg+SeMm+F6epatTeb695fRx1/H76bOoDO4fWZ0MN+HPeNDLXUFPPzHfOe1AZXiPIOHjnFt38c4i+9WxIRat2Zl87Ss3UDlMIjl5F8sHIfKcfy+b9xHStdLuguSimeurIT/aJCmfbdVnr/ZzGRj/xi+qV/6bn5fLJmP1f1CKfdeWqL3TYkipgmdXli7g7Dx4OuVlBsY96WNEZ3akrdAGNm9ZhF1xb1qePnbdplJJLAEJaitea53/bQsI4ftw6ONDocp2jTOJjsvMJqnUwWlth4bO52WoYGueRKlJeX4t/jOpGdV8gbyxKdvv+qKiopJeCsNqRlrFLLwIxuGxLNySIbX6w/YHQoTvHztjRu/mQDEQ2C+P6OAUSWW7dqRZUd2xqY9PYaFu06bIrlXUK406tL9uLtpbhreBujQzGFegG+dGhaz+MSGBm5Bby5LIkxnZsywHGV2Gi+3l6M69qcUm1f5qox/9K/15bsRWvNfZeev36Ir7cXz07qwuETBbV+FuDi3Yc5XlDC5J4RRodiOF9vL/pEhrImKdvoUCokCQxhKSv2ZrN23xHuGt6m1mRHo8s6kVRjbd17y/exL+skM8Z3qvQEv6a6RYQwuWcLPlqVTHJ29WeI1NSpohJu+Wwj+cU2fM66wmKVWgZm1bF5PYa0C+OTNfspKLb2+unP1x3g7q/i6NYihG9v70+TegFGh1Rj00bFEHjW8zrA14tJseEcPl7IrZ9tZNQrK/h+UwrFtlKDohTCfRIz8/ghLoUb+7WqFc9xZ+kTGcrmg8coKvGc14Hn5u/BpjWP1rDrmrO99XsSZ6eVzbr0z15HLoXr+7aq0mymHi0b8Lf+rfl83QFTFXh3ttmbUmheP4D+0Q2NDsUUBkQ3IinrJIdNWMRVEhjCMkpL7bMvWjQI5K99jV3z6ExtGtsTGElVrIOxP/skry9LZGzXZlwS09iVofHQqBj8vL14+pddLn2csx07WcRf31/Pqr1ZPD+5Ky9c3c1j2+W5ytQhUWSdKDTt1aEL0Vrz6uK9PDF3B8NiGvP5P/oaVsjN2Sb0COeZiV3OOOafndiVF6/pzu/TLuGVa7vjpRQPfLeVS2b9zserkz22kJ/wDK8sTiDA15upl0QbHYqp9I0MpaC4lB1puUaH4habDhzjh7hUbhscZbplRFZa7vrSwgRHHbmqz2Z6cFQMzeoFMH3OtlqZMDt8vIAVCVlcFRtu+LIkl0hPh6FDISOjyncpS+SsNeEyEh+jAxCiqn7alsau9OO8fG03/H1cM+vACBGhQfh5e1VpBobWmid+3IG/txf/d0VHl8fWuF4Ad49oy7O/7WF5QhZD24W5/DHTcvKZ8tEGDh49xTs39GRkp6YAkrBwsv7RDekSXp/3Vuzjml4ReFnoDbu0VPPUTzv5dO0BJsaG89ykrvh6V5yPt2qxvwk9wis85n29vZjQI5zx3ZuzLD6Tt5Yl8dRPu3h9aSI3DWjNlP6tCAmqWTtlIcxkd/pxft6Wzp3DomkUbP26V87UOzIUsNfBOLuLRG1TWqqZ8dNOmtTz5w4TJrKahwSSWkGywmzLXben5PLL9nTuGd6mWs+nYH8fZk7ozD8+3ci7y5O4e4Q131srMzculVINk2Ir7uhn1bHEaTNnwqpV9s9vvlmlu3RsVo/6gb6sSco23RhcZmAISygqKeXFhQm0b1qX8d3M9SSqKW8vRWSjOlWagfHztnRW7s3mwVExbptGe/PA1rRuGMTMn3e5fLp6WQvMw7kFfPb3PqeTF8L5lFLcNiSKfdknWbTbOtXFi0pKufebLXy69gC3DIrkhcndKk1eAAxq24hBbc2xTtqZlFIMb9+E2XcM4Lup/ekeEcJLixIY8OxS/vPzLtJzzXfVT4iL8dKiBOoG+HDbYPOdtBqtUbA/0WF1PKIOxpy4VLam5PLImPbU8Tff9deKlv4B3DnMXMft8wv20CDIl1uGRFX7viM6NGFs12a8vjSxVrUU1Voze1MKPVs1qLQ9s6XHEunp8PHHUFpq/1zFWRheXor+UQ1NWchTEhjCEr7+4yAHj57i4THtLXWluKraNA6+4AyM3PxiZvy8i64t6nNDv1Zuigz8fbx5fGxHEjPz+Hyt64o+/tkCU/P17f3oFyVrEF1tTOemRIQG8s7yJEt0tyirizJvaxoPj27PY2M7XPD1YGdaLjtr+fTq3q1D+eim3vz2r8GM7NiEj9fsZ8jzy3ho9tZaNcgUnmfroRwW7TrMrYOjas0SMWfrExnKH/uPYqvFhX3zCkt4bv4eerQMMe1FrLOX/oXV9UcBq5OOmOb9dU1SNiv3ZvPPS9pQ7yLryD05riMBvl5Mn7O91hST3paSy97MPCb3rHj2BVh8LDFzJhQV2b8uLrZ/X0UD2jQk5Vg+h46eclFwF0cSGML0ThaW8NqSvfSNDOUSNyxhMEJ0WB0OHT113oKKLy6M50heIU9P6OL29XkjOjRmcNtGvLw4gSN5hU7ff1kLzOAAH76/oz+dmtd3+mOIc/l4e3Hr4CjiDuaw0eSFucrXRXluUhfuuCQapS78PJjx0y5m/OTeGi5OU801qx2a1eOVv/Tg9wcv4bo+LflxSxqXvrScqZ9vYuuhHBcHK4TzvbgogQZBvtw8sLXRoSHdhfQAACAASURBVJhWn8hQThSUEJ9xwuhQXObNZYlknSjkyXGdTH0Ra0KPcFY/MpzkZ8fyx2OX8uCoGH7Zls6czcbXmtJa8/z8eJrVD+DG/hd/Eaxx3QAevbwDG5KP8u3GQ06M0DizN6Xg7+PF2K7NKr2NZccS5WdfAJSUwPvvV3lcMcBRB8Ns3UgkgSFM74OVyWTnFfHwmPZVOmGxoujGwZRq2H+k4m4fWw7l8Pm6A0zp35ouLdx/cq+U4slxHTlVZOPFRQlO3XdZC8yWoUF8P3UArRpauwWm1VzdM4IGQb68uzzJ6FAqlZ6bz9XvrmVX+nHevqEn1/auPUV8z6v8mtVqiAgNYsb4zqx+ZDj/vCSa1UnZjH9zNdd/sI5Ve7PRWjM3LpWBzy4l8pFfGPjsUssWcxW114bko6xIyGLq0Oha03XMFfpE2k8wNiSbb5q3Mxw4cpIPVyYzKbYF3SNCjA6nWqYOjaZP61CenLfT8CvYC3cdZsuhHO69tG2Nu9dd2zuCvpGh/PfX3WSeMF+HiuooLLExb2saozo1vehZKaY2c+afyYsyxcUwcWKV7h4dFkxYXX/TLSORBIYwtSN5hby3IolRnZrU6gJVf3YiOTeBUWIr5bEfttO4rj8PjDx/v25XatO4LlP6t+KrDQedNo2urAVm94gQvrm9P42lPZ7bBfp587cBrVm8O5O9h813BS8xM49Jb60hI7eAT2/uwyhPqYtykWtWy2sU7M+0Ue1Z88hwpo9pT8LhPG74cD2Dn7MvL0nNyUcDqTn5TJ+zXZIYwjS01rywMJ5Gwf5M6d/a6HBMLTwkkPCQQDbsr511MJ7+ZTe+3oqHR1uvdbq3l+Kla7uhgPu+2UKJQW2vbaWaFxbEExVWp9IildWhlOKZiV0oKCnlqXkWnJVQzpLdmeTmF593+YilrV375/KR8v74A45e+DVDKcWAaHsdDLMshQJJYFiSJ105e2NZIvnFNqaNst4bV3VENQpGKfvJ2tk+XXuAnWnHeXJcJ8OvQt07oh0Ngvx46qddNXohK98Cc0R7RwvMwFqY+baIKf1bE+Drxfsr9xkdyhn+rItSyte39fOs3uzlr5oUFcGMGRe9q7oBvtw+NJqVDw3jmYldSD9eQJHtzOdvfrGNWQviaxKxEE6zOvEIG5KPctewaAL9ak/XMVfpGxnKhuSjpjrBcIZVe7NZuOswdw5vY9kLHC0aBDFzQmc2HjjG278bM9Pxh7hU9mbm8eDIGHzOU/S6OqLCgrlneBt+2Z7O4l3WKQR+ttmbUmhaL4CBbSxaoPNC4uJA6zM/SkogMRFCQ+3jDNv527APiG5I1olCU9XUkgSGxcyNS2X6nO0eceXs0NFTfLHuAFf3jKBN47pGh+NSgX7ehIcEnlPIMz03n5cWxjMsJowxnY2/8lw/yJcHRrZjQ/JRftmeflH7KC3V/HveTl5enMCk2Ba8c0PPGk9nFDUTWsePa3tF8ENcKoePm2M6aPm6KLOnDqBzuAfVRSmbfVF21cRmg/feg7S0Gu02wNeb6/q0rLTwWloFLQCFcLey2RfN6wdwXV8PWS5WQ30iQ8nOK2JfdsXLUK2oxFbKjJ930jI0iL8PjDQ6nBqZ0COcK7s155Ule9ni5npEhSU2Xl6UQJfw+k4fR942JJqYJnV54scdnCgoduq+3SHzRAHLE7K4Kjbc7bXlDOXtDa0cdVCmTYNrroGCysd+A6LtyR0zLSORBIbFzFoQT/5ZhR5r65WzlxYl4KUU915m8d7LVRQdFnxOK9Wn5u3CpjUzxnc2Tf2Pv/RuSYdm9Xjm1z3kF50/a3u28i0wbx0cyazJXZ12NUDUzC2Do7CVaj5anWx0KPyyLf2MuiitG118XZSHRsfwkNWmHle0ZtVmg+HD7VdOaqh5SGC1tgvhTkv3ZLLlUA53j2iLv48kt6uid2QoQK1op1o2y7jNY7+RcDiPSzs0rhUXOWZO6EzTegHc+3UcJwtr/jpeVf9bd5DUnHweGh3j9HGkn48Xz0zqQsbxAl5c6Nz6aO7wY1watlJdpWU1lhxLVEVEBMyZA2PHwomKlxFHhAbRokEgaxIlgSGqKetEId9vSiG1kitkte3K2e7048zdkspNA1vTrL5nDKrbNA5mX3be6aujS3YfZv7ODO4Z0ZaI0CCDo/uTt5e9oGdqTj7vrqj6dMjyLTAfGdOex8Z2NHU1cU8TERrE5V2a8eW6gxw38ErK5+sOcNdXm+nWIoRvbqt5XZSerULp2SrUSdG5SWVrVuPja7SUpMy0UTEEnnVCEOjrXeuX6gnzKy3VvLgwgZahQbV3TboLRDWqQ6NgP/6weAKj/CzjMl9tOFgrZhnXD/TlxWu6ceDoKWb+7J66EXmFJby5LJEB0Q0Z5KIlErEtGzClXys+XbufzQfN3c2sPK01szel0KNlyOk6dOdjybFEVdx7L3z2GSxfbr9Ikl1xt5EB0Q1Zu++IaVrnSgLDpEpspfyx/yizFuzhitdX0vvpxTzw3VYqO9+rbVfOnp+/h7r+PvxzaBujQ3Gb6LBgCopLSc3J51RRCf/3407aNg7mlkFRRod2jn5RDRnbpRnvLE+qNKlWXvkWmM9P6srUodFuiFJU1+1DojlRWMJX6w+6/bHL10UZFuOoixJU87oomw4cZdMBiw3qK1qzqjV88QXcf3+Ndz+hRzjPTOxCeEggCnsRwGcmdmFCj/Caxy5EDczfmcGu9OPce2lbfGV2XpUppegTGcp6iycwKp5lXFprZhn3i2rI1KHRfP3HIebvqH5h5ur6cGUyR04W8dBo13bxmza6PU3rBTD9++0UlRhTqLS6dqQeJ/7wiSonSi05lqiqG2+EuXNhxw649NIKa2IMiG5Ebn4xu9KPGxDguXyMDkD8KSO3gBUJWfyekMnKvdmcKCjB20sR2zKEaaNiGNoujL0ZJ3h07o4zXuD9vL1q1ZWz9fuOsCw+i4dHt3fKCYxVlGWAE7Py+N/6o6Tm5PPt7f3x8zHnIG765e1ZvPswz/y6mzf+Glvp7dJz87nxww0cPHqKd27oyUhP6SJhQV1a1Gdgm4Z8tDqZmwa2dvn07blxqcxaEE9aTj5Bft6cLLIxMTac5yZ1ddrJy/Pz7QPfb27v75T9Ger66+2f8/Ph0UfhiSfsRbguwoQe4bU2YaGUigA+A5oCpcB7WutXlVKhwDdAa2A/cI3W+piyj+xfBS4HTgE3aa03O/b1N+Bxx67/o7X+1J2/iyexlWpeWpRAdFgdxnevncemK/VpHcqv2zNIOXaKFg3MM2uzOiqbTVybZhnfd2k7Vu7NYvqcbcS2DHFZcdKjJ4t4f+U+RnVq4vL2s8H+PswY35lbP9vIeyuSuGu4+Zd+z950CD8fL67o2rxKt69VY4mKXHEFLFwIWVn2GhlnKSuivjbpiClqkhlyZqSU+kgplamU2lFuW6hSapFSaq/jcwPHdqWUek0plaiU2qaUii13n785br/XMcgo295TKbXdcZ/XlFmKB5yl2FbK2qQjPPvbHka/soJ+zyzhoe+3senAMS7v3Iy3ro9l8xOX8d3UAdw5rA2dw+tzVc8WZ1w58/FS+PmoWlM9V2vNs/P30KSePzcNaG10OG6V4GhhefPHf/DO8iT6RjagT6R5p6u1aBDE7UOj+XlbeqXrbpOy8pj89loO5xbw2d/7SPLCAm4fEs3h44X8uKVmBSMv5OyCxCeLbPh4KQZHN5IrrxeyeTO89RZccslFtVf1ACXAA1rrDkA/4E6lVEfgEWCJ1rotsMTxPcAYoK3j4zbgbbCPS4Angb5AH+DJsrGJcL55W1NJzMzj/stiPKugnpP0ibSfYPxh4Xaqjer6V7i9Ns0y9vPx4pVre5BfbOPB2dtcNiX/rWWJnCoq4cGR7rnAeVnHJlzepSmvLU1kn4m6VVSksMTGj1vTGNmxiXTAK2/wYJg40f71t9/Cxo2nf9SkXgDRYXVYk1TxEhN3M2qU+Akw+qxtzhxYvO24bdn9zn4slzpfm9O0nHy+XH+Q2z/fSI8Zi7ju/XV8sHIfIUG+PDKmPb/9azDrpo/gucldubxLswqfWBN6hLP6keEkPzuWX+4ZTLFN8+B3W02zLqkmFu46TNzBHO67tJ1HtU6bG5fK07/sPmPb1pRc06/7vGNoNM3qB/DveTuxnXX82VtgrqWwxMZXt/WjX5QHtcC0sMFtG9GhWT3eW7HPZa8pBcU2Zvy865ypwiWlmhcWWa8QmNsNHAi//AL79sGgQbB/v9ERmYrWOr1sBoXW+gSwGwgHxgNlMyg+BSY4vh4PfKbt1gEhSqlmwChgkdb6qNb6GLAIN48nPEWxrZRXFu+lQ7N6pui4ZUUxTetSN8DHsoU8M48XUFhs4+zUVW2sz9OmcTCPje3IioQsPl273+n7T8vJ57N1B5gU24K2TdzXxe/f4zrh7+PF9DnbTd3Sd9meTHJOFUudncoUFdlneA4bBkuXnt48ILoRG5KPUmwzfpmQIQkMrfUK4OxXWKcMLBw/q6e1Xqvtz57Pyu3L5Spqc/rQ7G3c/PF6Rr68nAHPLuXRH7azPSWXcd2a884NPYn7v8v4+rb+TB0aTYdm9aq1Ti2maV0eH9uB5QlZfLxmv8t+L3cosZXy/Pw9RIfV8bgXlYrWfRZYYN1noJ830y/vwK7043y78dDp7WUtMOv4e3teC0yLU0oxdWgUiZl5LIvPdNp+84tszN+Rzj1fxdFz5iKOnqygSCW1a6qwS116KSxeDEeO2JMYu3df+D4eSCnVGugBrAeaaK3TwZ7kABo7bhYOHCp3txTHtsq2Cyf7flMKB46c4oHL2klx54vk7aXo3TrUkgmMgmIbt36+yX5BblSMR9TnuaFvS0a0b8wzv+0hPqPi7g8X69XFe0HDvZe1c+p+L6RxvQAevbwD65OPnjEmNJvZm1JoUs+fwW3DjA7FnPz8YNkyaN0axoyxdynBXsjzZJGNbSm5xsaHuWpgnDGwUEpd7MAi3PH12dvdoqIT0SJbKcvisxnYpiFX94xgaEwYbRsHO62gzg39WrFibzbP/baHvpGhlj1Z/H5zCklZJ3nnhliPa61p5XWf47o24+VF8Tz2w3YenbOdkCBfcvOLadekLp/9vY/L1ncK17m8SzOenx/Pu8v3MaJDk4vez8nCEpbuyWT+jgyW7skkv9hGaB0/ruzenIU7D3OkgiRGbZoq7HL9+tkrh19/vb3IpziDUioY+B64V2t9/DzvuRX9QJ9n+9mPcxv2WZ+0bNny4oL1YIUlNl5bspduESGM6ND4wncQleoTGcrSPZlk5xXSKLji5Rhmo7Xm4e+3sfVQDu/c0JPRnZty57DaX8BdKcVzk7sy+pUV/OvrOH68a6BT6k4lZubx3aZD3DQgknAD3k+v7RXBD45ZxcPaN6ZxXXONAbNOFLIsPotbBkfKUrXzad4cVqywt1e9+mp47z36/eVGANYmZdOzlbGrKa1wlljdgUWVBhxgH3QopTYqpTZmZWXVIMQ/VXbCqYD/3dKPW4dE0a5JXadWA1ZK8dykrjSo48s9X8dxqsh9/aWdpaDYxsuL9tI9IoRRHlgnobKTNiuczP24JY20nAJKtf2JduyUvQXnlAGtJHlhUb7eXvxjUCQb9h9l04HqtUU7XlDM3LhUbvtsI7EzF3H3V3GsTz7KpJ7hfHlLXzY8OoJnJnbliSs6uqWV5/+N68j/jevo1H2aSteusHUrdOxoT2IkJhodkSkopXyxJy/+p7We49h82DFLE8fnsilGKUBEubu3ANLOs/0MWuv3tNa9tNa9wsKce0XvfEtSa4uvNxwiLbeAB0e2c2mnBE9QVjfLSu1U3/o9iR+3pDFtVAyjPWz5UKNgf2ZN7saejBPMmu+cGbcvLown0NebO4cZ0+3Ny0vx36u6UFBcylM/uaddbHX8uCUVW6lmcmz1ZnrX+rFERRo0gEWLYORISE6mQR0/Ojarx5qkI0ZHZqoEhrMGFimOr8/efg5XDDqMOhENrePHy9d0Jzn7pNv6SzvTp2v2k3G8gIdd3OrJrKaNinHLyZwrzFoQT+FZbbNKNby5NMmgiIQzXNs7gvqBvry34sL/x5xTRXy38RB//+QPes1czL3fbGFbSi7X9WnJt7f3Z/2jI/jPhC4MaNPo9Owqd7Xy7NS8Pp2aW3NWWpV5Od7K33gDunSBX381Nh6DOQp3fwjs1lq/VO5H84Cygt9/A34st32Ko2h4PyDXMSN0ATBSKdXAUWNrpGObW1S0JHX6nO21KomRX2TjjWWJ9IkMZVAtKUZupM7N6xPo622ZdqoLd2Ywa0E8V3Zrzj8v8cz26sPaN+bGfq34YFUyq/bWrEDi1kM5/LYjg1sGR9HQwBk4bRoHc9fwNvyyLZ0luw8bFsfZtNbM3pRCt4iQatcG8YixREXq1IF582DmTABG1y1k4/6jFBSf22rVncy0hKRsYPEs5w4s7lJKfY29YGeuY4nJAuC/5Qp3jgSma62PKqVOOAYh64EpwOvu+iWmjYph+pztZywjcdeJ6IA2jZg6NJq3f09icNswLu/SzOWP6Qy5p4p56/ckhrYLO92mx9OUnbSVtZRsHhLItFExllj3aeXlL6Jydfx9mNK/Fa8vTaTP04vJOlF4xnF59GQRC3dm8OuODNYkZlNSqgkPCWRK/1aM6dKMHhEhF1zL7o5WnmUDwkFtPeDk6C9/gU8+gfHj4Ysv4NprjY7IKAOBG4HtSqktjm2PYh9ffKuU+gdwELja8bNfsbdQTcTeRvVmAMd4Yibwh+N2M7TWbjszrGhJan6xjVkL4i3x3lAVn6/bT9aJQt64rodHXrxwNj8fL3q0DLFEHYxdace595stdGtRn+cnd/Xo//+jl3dgTVI2D3y3hQX3DiEkyO+i9jNrQTyhdfy4ZXCkkyOsvqlDo/l5WxpPzN1B36iGBPsbf8q5M+04ezJOMHNC52rf16PGEmfzdTSUyMhg6rS/0DS8B5unxDIgxrgZU4YcTUqpr4BLgEZKqRTs3UScObC4A3unk0DgN8eHWxh9Inr/Ze1Yk5jNI99vo1tEiCHr36rr7eVJHC8o5uHR7Y0OxVDuOJlzheYhgaRWkKywwvIXcX6N69mv4GSeKATsV4Cnzd7Km8v2si/7FLZSTcvQIG4ZHMWYzk3p2qK+6Qahry/dC3jIoCMszF4xfNw4uO46yM2F224zOiq301qvouLlpAAjKri9Bu6sZF8fAR85L7qqqywJnJqTz5zNKYzq1JQ6JjgpuFh5hSWOCy6N6CtdqpymT2Qory7ZS25+sWlbRGbnFXLrZxupF+DL+1N6EeDrOV3nKhLo582rf+nBVW+t5tEftvPmX2Or/V66OjGbVYnZPHFFR+oGGP9/9/Px4pmJXZn8zhpeWBDPv6/sZHRIzN6Ugp+3F+O6Vv8Cr0eNJSrTpAn6jju45un/sPfvN8KSnyDAmKXihrzzaa2vq+RHThlYaK03AtVPrzmJkSeivt5evHZdDy5/dSX3fb2Fr27rZ+oiNRm5BXy8Opnx3ZrTsXk9o8MRF8HIWUfCtd75fd8524ptmuTsU9wxNJoxXZrSsZqdk4SL1a8P8+fD5Mlw550wYgREe+bUbKurLDns7aW4/9utBPntYHTnpkyKbUG/qIamfq+vyMerkjl2qpgHRsp7hTP1iQxFa9h84BjD2puvKGphiY2pn2/iyMlCvrt9gNTKcugcXp8HRsbw7G97mL0phat7RVz4Tg5aa56fv4fm9QO4vq95Cgn3bNWAG/q24tO1+xnfvTk9WhpX+LGopJR5W9O4rGOTi57h4vGUwv8/M3k/IY9bv3sFLr8c5s6Feu4/fzNTDQzhJK0a1mHmhM5s2H+UN5eZu6Dbq0sSKNVaBjAW5q5aBsL9KrsCbCu1t7rr1Nx8My4EEBRkH1QsXizJCwurrDbSC5O78t3U/ozv3pxFuw5z/QfrGfTcUp79bQ97Dzu3HaOr5J4q5r2V+7i0QxO6R4QYHU6t0iOiAb7eypR1MLTWPPbDDjYeOMYLV3ejSwsPrClwHrcOjqJfVCj/nreTA0dOVvl+C3ZmsDUll3sva2e62SwPjY6hSd0Aps/ZTrGt9MJ3cJFl8ZkcPVnE5J7VK94pzpXzj9u5f9yD6JUr7bUx0tNh6FDIyHBbDNadeyjOa2JsC1YkZPHqkr0MbNOQnq1CjQ7pHElZeXy7MYUb+7UiIjTI6HBEDVh1+Ys4P1keZGF+fvYBBdiTGUuXwiuv/FnwU5jehZak9m4dypPjOrFkdyZzNqfw/sp9vLM8iS7h9ZkYG86V3ZobWsjvfN5fuY8TBSXcf1k7o0OpdQL9vAkPCeTDVft4d3mSqWpqfbAymdmbUrhnRFuu6Nrc6HBMx9tL8eI13Rn9ygru+2YL397e/3Th68qU2EqZtSCe6LA6TDTB//hsdQN8mTG+E7d9vomeMxdxoqDEkGNy9qYUwur6M9iTl4A4yYDoRlzf8RJuuGYQsRMvgwcfhFWr7MmMN990SwwykqnFZk7oTPOQAO75agu5+cVGh3OOFxbEE+DjxV3Da3+/byGsyMrdcUQ5f/wBr78OU6ZAsfneC0TlJvQIZ/Ujw0l+diyrHxl+zoA/wNebsV2b8eFNvVn/6Aj+74qOaDRP/bSLvv9dwi2f/sGv29MNrxhf3pG8Qj5anczYrs1k6agLzI1LJeVYPsU2baruNcv2ZPLf33ZzeZem3DuiraGxmFl4SCBPX9WFzQdzeKMKs6jnxKWSlHWSaaNiLpjsMMqpIhteCo4XlBhyTB7JK2TZnkyu6hFu2r+RlfRs1QA/by/mh7az19r6+GMoLbV/dtMsDJmBUYvVDfDl1b/04Op31vLYD9t53QRVvufGpZ6+mqSBUR2b0MikV4iE8HRGFyV2hv9O7GJ0CMb7z38gOBgefRROnIBvvjGs8JZwnUbB/vx9UCR/HxRJfMYJ5sSlMDculcW7M6kX4MMV3ZozKTac2JYNUEqd8X7szuf2O8uTKCi2cd+lchLrCrMWxFNSqs/YZnT3moTDJ7j7qzg6NqvHC1d3u2CHKk93ZbfmLNuTyetLExnSLozYSmpHFBTbeGVRAt1a1GdUJ+M6QlzIrAXxnHVIuvWY/HFLGiWlmkmxF798RMYSfwrw9Sa2VQhrkrLh59fB5kiQ22xum4UhCYxaLrZlA+6/rB2zFsQztF1YtYoCOVtZT/vyxR6X781iblyqpU6IhPAkVl8eFB0WbHQIxlMKpk+3F/i880574a1ff4Vjx+ytV7/5Bpqad/Arqi+maV2mj+nAQ6PasyYpmzmbU/lhcypfrj9Iq4ZBdGhaj2XxmRSW2Nekl10RBVz6fD98vIDP1h5gQo9w2jSu67LH8WRma21+9GQRt3y6kQBfb96f0osgPzn1qIqnxndiQ/JR7vtmC7/cM7jCNqRfrDtAWm4Bs67uZvgFyvMx+picvSmFri3qE9P04l9zZCxxpgHRjfhi7jr0xx+jymZ2FhXZZ2E88YTLxxQyj8YDTB0aTb+oUJ6ct5N9WXmGxVFRT/uCYvvaPSGEcIXFuw6zeNdho8Mwh3/+E774AmJjwd/ffqWkbN2qqJW8vRSD24bx8rXd+ePxS3nh6m6EhwQyf2fG6eRFmbIroq705rJEbKWaf8kSApeprEZRHX9vThWVuDWWopJS7vhiExnHC3h/Sk+pn1QN9QJ8eeUv3Tl09BQzftp5zs9PFBTz1u9JDGrTiIFtzF3XobL/e5C/N4Ulrl3etjMtl13px2tcvFPGEmfqH92Qu1d9RenZhVnLZmG4mCQwPIC3l+Lla7vj5+PFv77eQlGJMVWAjc7ACiE8z/sr9/H+ynPbwXqs66+HF16wr1P96CO3r1sVxgn292FyzxZ8eWs/KrtWm5qTz6Gjp1zy+CnHTvHVhoNc3SuCVg3ruOQxRMW1i7y9FHmFNi57aQXL9mS6JQ6tNU/O28n65KM8N6mLoS00rap361D+eUkbvt2Ywvwd6Wf87IOVyRw9WWSJmlSVHZMnC21MensNydlV77hSXd9vSsXXWzGuhkVjZSxxpm4tQuiVHo93cdGZPygqgjVrXP74ksDwEM3qB/LcpK5sT83lxYXun/GwcGcGlc1uk4y8EEK42VNP/VnQ001XTIR5nO99d/Dzyxj9ygpeXpTArrTjaK0rvW11vL4kEYXibinc7VIVtTZ/8epufDe1P4F+3tz8yR/c+eVmMk8UuDSOT9fs56sNB7njkmiu6iGtKy/Wvy5tS7cW9XlkznYycu3/syN5hXywch9jOjelmwXaEFd2TL57Y08OHc3nitdW8kNcitMft9hWyo9bUrm0QxMa1PFz+v49mZ+PF8/89ysue/F30PrMj7g4lz++LETzIKM6NeX6vi15d8U+BrVtxOC2YS5/zIzcAv49byfzd2bQrJ4/R08VnzFtVToaCCGEm6Wnw6ef2mdfgFvXrQpzmDYq5pyaVIG+3jw4sh0aWLjzMK8t3curS/YSERrIyI5NGdmxCb1ah+J9EQUYk7NPMnuzvW26XLRwvcpqF/1yzyDeXb6PN5YlsiIhi0fGtOe63i2dXlRzRUIWM37exWUdmzBtpIzxasLX24uXr+3O2NdWMeXD9eQVlZCWY09kdLdA8qJMZcdk5/D63Pt1HPd9s5VVe48wY3wn6lRQ7+Ni/B6fxZGTRTVePiIqNiC6Ic/+tofMEwU0ruvewuAyA8PDPD62I20bB3P/t1s5klfosscpLdV8vu4Al720nGXxmTw8uj0rHh7Oc5O6npGBfWZiF0sXCBRCCMuZOfPP5EUZmYXhUSq6IvrMxC78Y3AUtwyO4tup/fnjsUt5blIX2jWuy+frDnDte+vo/fRiHpq9lSW7D1erNeurixPw9Vb8c1i0634pcUH+Pt7cM6It8/81mM7N6/PYDzu45t21JBw+4bTHSMrK484vN9OuSV1evra7dBxxvYvqDQAAE3dJREFUgqiwYK7o1oyEzLzTyQuAVxbvNbw9bk2FhwTy1a39uGd4G+bEpTDu9VXsTMt1yr5nbzpEo2B/hrRz/QVbTzQguiEAa5OOuP2xZQaGhwn08+a163ow/s3VTJu9jQ//1svplYsTDp9g+pztbDpwjEFtGvH0VZ1Pr3e1ekcDIYSwvLVr7bMuynPTulVhHhd6P24U7M+1vVtybe+W5BWWsDw+i4W7MvhtewbfbkwhyM+bS2LCGNmxKcPaN6Z+oG+F+9l7+AQ/bk3jtiFRbr9KJyoWFRbMl7f2ZfamFJ7+dTdjX1vJ7UOiuWt4GwLOqlVQHbmnirnl0434eXvx/pReFXbOEBdnTWL2OduMbo/rLD7eXtw/MoZ+0Q259+stXPXWGh67vANT+re66HOUoyeLWLonk7/1b42vt1yvd4VOzetTN8CHtUlHGN/dvcegvLJ4oA7N6vHomPb8+6ddfLpmPzcNjHTKfguKbby5LJF3licR7O/DS9d046oe4aZu7SSEqN1evra70SGYjxvWp4raJdjfh7FdmzG2azOKSkpZt+8IC3ZmsGjXYX7dnoGPl6J/dENGdrIvNWlSL4C5canMWhBPak4+CmjZIMjoX0OUo5Ti6l4RDG/fmKd/2c0byxL5eVsa/72qCwMuoqtFia2UO7/cTMqxU3x5az8iQuX/7UzlZ16cub32FMIfEN2I3/41mAe/28qT83ayKjGbWZO7EhJU/foV87akUmzTTHLS8hEZS5zL20vRL6ohawyYgaGcVZzJ6nr16qU3btxodBhuo7XmH59uZFViNj/eOZAOzerVaH9rk47w6A/bSc4+ycTYcB4f25FQKZgjhBAeRym1SWvdy+g4jOBpY4nSUs2WlBwW7jzMwp0Z7HN0E2gZGkhaTgElpX+OMQN9vWXZqImt2pvNY3O3c+DIqYsax/173k4+WbOf5yd15ZreES6M1DMNfHYpqRUkK8JDAln9yHADInKd0lLNR6uTeW7+HsKC/Xn1uh70bh1arX1c8fpKAH6+e7ArQhQOH69O5qmfdrHyoWEuSVpWNp6QOTUeSinFrMldqR/oy91fxZFfdHF9mHNOFfHQ7K1c9/46bKWaL/7Rl5eu6S7JCyGEKfy0NY2ftqYZHYYQtZKXlyK2ZQMeGdOeJQ8MZfH9Q5g2Kob03DOTF/DndHdhToPaNmLBvUO4c1g087akMeLF35m9KaVKXWj+t/4An6zZzy2DIiV54SIVtSKtrYXwvbwUtwyOYvbUAfh4e3Htu2t5bclebKVVu+i+O/04O1KPMznWecU7ZSxRsbJaSIOfX8bAZ5e6rSaLJDA8WMNgf166phuJmXn855dd1bqv1tremuil5Xy/OZU7Lolmwb1DGNS2+tMOhRDCVb5Yd4Av1h0wOgwhaj2lFG0a1+XOYW0osVV8olGbprvXRgG+3kwb1Z5f7hlMVFgwD363les/WE+yY2ZNRdYkZfPkjzu5JCaM6Zd3cGO0nqWywru1eUZTt4gQfrlnEFd0bc5LixK44YP1HD5+4fa/329KwddbcaUT6zLIWOJcc+NSeW3J3tPfp+bkM33OdrckMaQGhocb3DaM24dE8e6KfQxuG8bozhduoXfo6Cken7uD5QlZdGtRn8/+3peOzWu2BEUIIYQQtUPzkMAKp7tLC1VriGlal+9u78+XGw7y3Pw9jHplBXcPa8PtQ6Px8/nz2ueBIyf55/8207pRHV67rsdFtdgVVeeJhfDrBvjy6l+6M6htI578cSdjXl3Ji1d3Y1j7xhXevthWytwtaQxv31hmg7vYrAXx5Bef2dHMXYVlJYEheGBkDGuSjvDInG10i6hPs/oVDzBKbKV8vHo/Ly1KwEvBk+M6MqV/a3nDEkIIIcRp00bFMH3OdvLLtVqtrdPdaysvL8UN/VoxsmMTnvppFy8uSmDe1jTGdGnK95tSScvJx9tL4eut+PBvvagXUHEXGiFqSinFNb0iiG0Zwl1fxnHzJ39wy6BIHhrd/oyEGsCKhCyy8wqZ3FOWMrlaZTPq3DHTTpaQCPx8vHjtuh4UlZRy3zdbKlxjtj0ll/FvrubpX3czsE1DFt0/lJsHRkryQgghhBBn8MTp7rVV43oBvHl9LB/d1IusE4W8tiSR1Jx8NFBSqrFpiDuYY3SYwgO0aVyXuXcO5MZ+rfhgVTKT31nD/rOWN83elELDOn5cEhNmUJSeo7IZde6YaSczMAQAkY3q8NSVnZg2exv3fLWZLYdyScvJp2n9AGKaBLNibzYNg/156/pYxnRuKq1RhRBCCFEpT5zuXpsNb9+EQD9vcvKLz9heVFLqlinjQoC9TsvMCZ0Z2KYRD83eyhWvr+LpqzqjNTw3fw/puQXU8ffml23pcky6mJEz7SSBIU6b3LMFX64/yC/bM05vS88tID23gP7RobxzQy/qB8oUQSGEdbx9Q0+jQxBCiFohI7fiAopSnFW42+jOTekcXo9/fb2Ff329BW8vdXoG+clCG9PnbAdwWhJDxhLnKvvbzloQT1pOPs1DApk2KsYtiSNJYIjTlFJkVFLd9+CRfEleCCEsR4p4CSGEc0hxVmEmLRoE8c1t/egxYxEnCkvO+Jmzi0nKWKJiRs20kxoY4gySXRdC1CbfbTzEdxsPGR2GEEJY3rRRMQT6ep+xTYqzCiP5eHuRd1byoowzz11kLGEuksAQZzCyIIsQQjjb7E0pzN6UYnQYQghheVKcVZiRO85dZCxhLrKERJxBWp8JIYQQQoiKSHFWYTZy7uJ5JIEhzmBkQRYhhBBCCCGEqCo5d/E8ksAQ55DsuhBCCCGEEMIK5NzFs0gNDCGEEEIIIYQQQpiezMAQQghRa31ycx+jQxBCCCGEhclYwlwkgSGEEKLWCvTzvvCNhBBCCCEqIWMJc5ElJEIIIWqtz9fu5/O1+w2OQliZUmq0UipeKZWolHrE6HiEEEK4l4wlzEUSGEIIIWqtn7el8/O2dKPDEBallPIG3gTGAB2B65RSHY2NSgghhDvJWMJcJIEhhBBCCFGxPkCi1nqf1roI+BoYb3BMQgghhMeSBIYQQgghRMXCgUPlvk9xbDtNKXWbUmqjUmpjVlaWW4MTQgghPI0kMIQQQgghKqYq2KbP+Ebr97TWvbTWvcLCwtwUlhBCCOGZJIEhhBBCCFGxFCCi3PctgDSDYhFCCCE8ntJaX/hWHkAplQUccPJuGwHZTt6nO1k5fondGFaOHawdv8RuHCvH74rYW2mta8VUBKWUD5AAjABSgT+Av2qtd1Zye1eMJUCOMaNYOXawdvwSuzGsHDtYO36J/VwVjid8XPBAluSKwZZSaqPWupez9+suVo5fYjeGlWMHa8cvsRvHyvFbOXZ30FqXKKXuAhYA3sBHlSUvHLd3SeLGyv8nid04Vo5fYjeGlWMHa8cvsVedJDCEEEIIISqhtf4V+NXoOIQQQgghNTCEEEIIIYQQQghhAZLAcK33jA6ghqwcv8RuDCvHDtaOX2I3jpXjt3LsnsTK/yeJ3ThWjl9iN4aVYwdrxy+xV5EU8RRCCCGEEEIIIYTpyQwMIYQQQgghhBBCmJ4kMGpAKfWRUipTKbWj3LZZSqk9SqltSqkflFIh5X42XSmVqJSKV0qNMibq07FUOXal1GVKqU1Kqe2Oz8ONi7z6f3fHz1sqpfKUUg+6P+IzXcRx01UptVYptdPxPwgwJvJqHze+SqlPHTHvVkpNNypuRzwVxT7TEfcWpdRCpVRzx3allHrN8XzdppSKNS7y07FWJ/7rHdu3KaXWKKW6GRd59WIv9/PeSimbUmqy+yM+I45qxa6UusSxfadSarkxUf+pmsdNfaXUT0qprY74bzYucs9h5bGEIx4ZTxhAxhLGsfJ4QsYSxrHyeMJ0YwmttXxc5AcwBIgFdpTbNhLwcXz9HPCc4+uOwFbAH4gEkgBvi8TeA2ju+LozkGqVv3u5n38PfAc8aLHjxgfYBnRzfN/QQsfNX4GvHV8HAfuB1iaLvV65r+8B3nF8fTnwG6CAfsB6kx43lcU/AGjg+HqM0fFXJ3bH997AUuydHyZbJXYgBNgFtHR839hix82j5Z6/YcBRwM/o36G2f1TzddVUY4mLiF/GE8b83WUs4fr4LTGeqGbsMpYw7m9vqvFENWN3+VhCZmDUgNZ6BfZ/SvltC7XWJY5v1wEtHF+Px/4CXKi1TgYSgT5uC/Ys1Yldax2ntU5zbN8JBCil/N0W7Fmq+XdHKTUB2Ic9dsNVM/6RwDat9VbH7Y5orW1uC/Ys1YxdA3WUUj5AIFAEHHdXrGerJPby8dTBHjPYn6+fabt1QIhSqpl7Iq1YdeLXWq/RWh9zbD/j+WCEav7tAe7GfpKQ6frozq+asf8VmKO1Pui4ndXi10BdpZQCgh33K0G4lJXHEiDjCaPIWMI4Vh5PyFjCOFYeT5htLOHjzJ2Jc/wd+MbxdTj2J3+ZFMc2syofe3mTgDitdaGb46mO07ErpeoADwOXAYYvH6mi8n/7doBWSi3AnsX8Wmv9vGGRXVj52Gdjf+NOx37V5D6t9dHK7mgUpdTTwBQgFxjm2BwOHCp3s7Lna7p7o7uwSuIv7x/Yr/6YTkWxK6XCgauA4UBv46I7v0r+7u0AX6XU70Bd4FWt9WfGRHh+lcT/BjAPSMMe/7Va61JjIhTlWHksATKeMIqMJdzMyuMJGUsYx8rjCaPGEjIDw0WUUo9hzzb9r2xTBTczZQuYCmIv294J+7S+242IqyoqiP0p4GWtdZ5xUVVdBfH7AIOA6x2fr1JKjTAovPOqIPY+gA1ojn2q8wNKqSiDwquU1voxrXUE9rjvcmy2zPO1kvgBUEoNwz7oeNiI2C6kkthfAR428upgVVQSuw/QExgLjAKeUEq1MyjE86ok/lHAFuzP2e7AG0qpegaFKLD2WAJkPGEUGUsYw8rjCRlLGMfK4wmjxhKSwHABpdTfgCuA67XWZS9SKUBEuZu1wJ6ZMpVKYkcp1QL4AZiitU4yKr7zqST2vsDzSqn9wL3Ao0qpuyrZhaHOc9ws11pna61PYV/HZ3hBybNVEvtfgfla62LH1LfVQC+jYqyCL7FfEQSLPF/PUj5+lFJdgQ+A8VrrI4ZFVTXlY+8FfO14zk4G3nJM2zars4+b+Vrrk1rrbGAFYGjRsyooH//N2Kesaq11IpAMtDcsMg9n5bEEyHjCKDKWMAUrjydkLGEcK48n3DqWkASGkymlRmPPUF7peJMoMw/4i1LKXykVCbQFNhgRY2Uqi13ZK0H/AkzXWq82Kr7zqSx2rfVgrXVrrXVr7NnY/2qt3zAozEqd57hZAHRVSgU51n8OxV7UxzTOE/tBYLiyq4O9eNUeI2KsjFKqbblvr+TP+OYBUxyx9wNytdammu4JlcevlGoJzAFu1FonGBHbhVQWu9Y6stxzdjbwT631XANCrNR5jpsfgcFKKR+lVBD2E57d7o7vQs4T/0FghOM2TYAY7Ov9hZtZeSwBMp4wiowljGPl8YSMJYxj5fGEoWMJbXBFVit/AF9hX8NWjD1T9g/sBbUOYZ86s4Uzq+E+hr1ieDwwxiqxA48DJ8tt34KB1XCr+3cvd79/Y44uJNU9bm7AXjBsB/C8VWLHXrjnO0fsu4BpJoz9e8ffdRvwExDuuK0C3nQ8X7cDvUx63FQW/wfAsXL/k41Wif2s+32C8V1IqhU7MM1xvO8A7rXYcdMcWOg45ncANxgdvyd8XMR7gmnGEtWNHxlPGHncyFjCtfFbYjxRzdhlLGFg/JhoPFHN48blYwnleCAhhBBCCCGEEEII05IlJEIIIYQQQgghhDA9SWAIIYQQQgghhBDC9CSBIYQQQgghhBBCCNOTBIYQQgghhBBCCCFMTxIYQgghhBBCCCGEMD1JYAghagWl1O9KqV5GxyGEEEII65LxhBDmJgkMIYQQQgghhBBCmJ4kMIQQhlBKPaSUusfx9ctKqaWOr0copb5QSo1USq1VSm1WSn2nlAp2/LynUmq5UmqTUmqBUqrZWfv1Ukp9qpT6j/t/KyGEEEK4k4wnhPAsksAQQhhlBTDY8XUvIFgp5QsMArYDjwOXaq1jgY3A/Y6fvw5M1lr3BD4Cni63Tx/gf0CC1vpx9/waQgghhDCQjCeE8CA+RgcghPBYm4CeSqm6QCGwGfvAYzAwD+gIrFZKAfgBa4EYoDOwyLHdG0gvt893gW+11uUHIUIIIYSovWQ8IYQHkQSGEMIQWutipdR+4GZgDbANGAZEA8nAIq31deXvo5TqAuzUWvevZLdrgGFKqRe11gUuC14IIYQQpiDjCSE8iywhEUIYaQXwoOPzSmAqsAVYBwxUSrUBUEoFKaXaAfFAmFKqv2O7r1KqU7n9fQj8CnynlJIErRBCCOEZZDwhhIeQBIYQwkgrgWbAWq31YaAAWKm1zgJuAr5SSm3DPgBpr7UuAiYDzymltmIfnAwov0Ot9UvYp49+rpSS1zghhBCi9pPxhBAeQmmtjY5BCCGEEEIIIYQQ4rwkmyiEEEIIIYQQQgjTkwSGEEIIIYQQQgghTE8SGEIIIYQQQgghhDA9SWAIIYQQQgghhBDC9CSBIYQQQgghhBBCCNOTBIYQQgghhBBCCCFMTxIYQgghhBBCCCGEMD1JYAghhBBCCCGEEML0/h8qg9qDG2wy1wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot sample forecast results\n",
    "num_samples = 6\n",
    "min_week = 120\n",
    "sales = pd.read_csv(os.path.join(DATA_DIR, \"yx.csv\"))\n",
    "sales[\"move\"] = sales[\"logmove\"].apply(lambda x: round(math.exp(x)) if x > 0 else 0)\n",
    "\n",
    "plot_predictions_with_history(\n",
    "    combined,\n",
    "    sales,\n",
    "    grain1_unique_vals=store_list,\n",
    "    grain2_unique_vals=brand_list,\n",
    "    time_col_name=\"week\",\n",
    "    target_col_name=\"move\",\n",
    "    grain1_name=\"store\",\n",
    "    grain2_name=\"brand\",\n",
    "    min_timestep=min_week,\n",
    "    num_samples=num_samples,\n",
    "    predict_at_timestep=max(train_df.week),\n",
    "    line_at_predict_time=True,\n",
    "    title=\"Prediction results for a few sample time series (predictions are made at week 135)\",\n",
    "    x_label=\"week\",\n",
    "    y_label=\"unit sales\",\n",
    "    random_seed=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional Reading\n",
    "\n",
    "\\[1\\] Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. LightGBM: A highly efficient gradient boosting decision tree. In Advances in Neural Information Processing Systems. 3146–3154.<br>\n",
    "\\[2\\] Alexey Natekin and Alois Knoll. 2013. Gradient boosting machines, a tutorial. Frontiers in neurorobotics, 7 (21). <br>\n",
    "\\[3\\] The parameters of LightGBM: https://github.com/Microsoft/LightGBM/blob/master/docs/Parameters.rst <br>\n",
    "\\[4\\] Anna Veronika Dorogush, Vasily Ershov, and Andrey Gulin. 2018. CatBoost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363 (2018).<br>\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "author_info": {
   "affiliation": "Microsoft",
   "created_by": "Chenhui Hu"
  },
  "kernelspec": {
   "display_name": "forecasting_env",
   "language": "python",
   "name": "forecasting_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
